@antv/algorithm - npm Package Compare versions

Comparing version 0.1.21 to 0.1.22

dist/async.min.js

@@ -1,2 +0,2 @@
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t.edges.forEach((function(e){(r.directed||t.label<=o[e.to].label)&&n.push(e)})),n},e.prototype.findBackwardEdge=function(e,t,r,n){if(!this.directed&&t===r)return null;for(var o=e.nodeMap,i=o[r.to].edges,a=i.length,d=0;d<a;d++){var s=i[d];if(!n.hasEdge(s)&&s.to===t.from)if(this.directed){if(o[t.from].label<o[r.to].label||o[t.from].label===o[r.to].label&&t.label<=s.label)return s}else if(t.label<s.label||t.label===s.label&&o[t.to].label<=o[r.to].label)return s}return null},e.prototype.findForwardPureEdges=function(e,t,r,n){for(var o=[],i=t.to,a=e.nodeMap[i].edges,d=a.length,s=0;s<d;s++){var u=a[s],c=e.nodeMap[u.to];r<=c.label&&!n.hasNode(c)&&o.push(u)}return o},e.prototype.findForwardRmpathEdges=function(e,t,r,n){for(var o=[],i=e.nodeMap,a=i[t.to].label,d=i[t.from].edges,s=d.length,u=0;u<s;u++){var c=d[u],f=i[c.to].label;t.to===c.to||r>f||n.hasNode(i[c.to])||(t.label<c.label||t.label===c.label&&a<=f)&&o.push(c)}return o},e.prototype.getSupport=function(e){var t={};return e.forEach((function(e){t[e.graphId]||(t[e.graphId]=!0)})),Object.keys(t).length},e.prototype.findMinLabel=function(e){var t=void 0;return Object.keys(e).forEach((function(r){var n=e[r],o=n.nodeLabel1,i=n.edgeLabel,a=n.nodeLabel2;t?(o<t.nodeLabel1||o===t.nodeLabel1&&i<t.edgeLabel||o===t.nodeLabel1&&i===t.edgeLabel&&a<t.nodeLabel2)&&(t={nodeLabel1:o,edgeLabel:i,nodeLabel2:a}):t={nodeLabel1:o,edgeLabel:i,nodeLabel2:a}})),t},e.prototype.isMin=function(){var e=this,t=this.dfsCode;if(this.verbose&&console.log("isMin checking",t),1===t.dfsEdgeList.length)return!0;var r=this.directed,n=t.toGraph(-1,r),o=n.nodeMap,i=new Z,a={};n.nodes.forEach((function(t){e.findForwardRootEdges(n,t).forEach((function(e){var r=o[e.to],i="".concat(t.label,"-").concat(e.label,"-").concat(r.label);a[i]||(a[i]={projected:[],nodeLabel1:t.label,edgeLabel:e.label,nodeLabel2:r.label});var d={graphId:n.id,edge:e,preNode:null};a[i].projected.push(d)}))}));var d=this.findMinLabel(a);if(d){i.dfsEdgeList.push(new Y(0,1,d.nodeLabel1,d.edgeLabel,d.nodeLabel2));var s=function(a){for(var d=i.buildRmpath(),u=i.dfsEdgeList[0].nodeEdgeNodeLabel.nodeLabel1,c=i.dfsEdgeList[d[0]].toNode,f={},h=!1,l=0,v=r?-1:0,p=function(t){if(h)return"break";a.forEach((function(r){var o=new $(r),a=e.findBackwardEdge(n,o.edges[d[t]],o.edges[d[0]],o);a&&(f[a.label]||(f[a.label]={projected:[],edgeLabel:a.label}),f[a.label].projected.push({graphId:n.id,edge:f,preNode:r}),l=i.dfsEdgeList[d[t]].fromNode,h=!0)}))},g=d.length-1;g>v&&"break"!==p(g);g--);if(h){var b=e.findMinLabel(f);i.dfsEdgeList.push(new Y(c,l,V,b.edgeLabel,V));var E=i.dfsEdgeList.length-1;return e.dfsCode.dfsEdgeList[E]===i.dfsEdgeList[E]&&s(f[b.edgeLabel].projected)}var y={};h=!1;var m=0;a.forEach((function(t){var r=new $(t),i=e.findForwardPureEdges(n,r.edges[d[0]],u,r);i.length>0&&(h=!0,m=c,i.forEach((function(e){var 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r="".concat(o,"-").concat(t.label,"-").concat(u[t.to].label);a[r]||(a[r]={projected:[],fromNodeId:o,edgeLabel:t.label,nodeLabel2:u[t.to].label}),a[r].projected.push({graphId:e.graphId,edge:t,preNode:e})}));var v=function(r){t.findForwardRmpathEdges(s,c.edges[n[r]],i,c).forEach((function(o){var i="".concat(t.dfsCode.dfsEdgeList[n[r]].fromNode,"-").concat(o.label,"-").concat(u[o.to].label);a[i]||(a[i]={projected:[],fromNodeId:t.dfsCode.dfsEdgeList[n[r]].fromNode,edgeLabel:o.label,nodeLabel2:u[o.to].label}),a[i].projected.push({graphId:e.graphId,edge:o,preNode:e})}))};for(f=0;f<n.length;f++)v(f)}})),Object.keys(d).forEach((function(e){var r=d[e],n=r.toNodeId,i=r.edgeLabel;t.dfsCode.dfsEdgeList.push(new Y(o,n,"-1",i,"-1")),t.subGraphMining(d[e].projected),t.dfsCode.dfsEdgeList.pop()})),Object.keys(a).forEach((function(e){var r=a[e],n=r.fromNodeId,i=r.edgeLabel,d=r.nodeLabel2;t.dfsCode.dfsEdgeList.push(new 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l=c?c[h]:void 0;if(!l){var v=((f={})[h]={start:n[t.id].idx,end:n[r.id].idx,distance:o},f);l=(c=oe(v,i,e,c))[h]}return ie(l,a,d,s)},ce=function(e,t,r,n){var o,i,a,d=null===(o=e[t])||void 0===o?void 0:o.degree,s=null===(i=e[t])||void 0===i?void 0:i.inDegree,u=null===(a=e[t])||void 0===a?void 0:a.outDegree;return void 0===e[t]&&(d=1/0,s=1/0,u=1/0,n[t].forEach((function(e){var t=r[e.id].degree;d>t&&(d=t);var n=r[e.id].inDegree;s>n&&(s=n);var o=r[e.id].outDegree;u>o&&(u=o)})),e[t]={degree:d,inDegree:s,outDegree:u}),{minPatternNodeLabelDegree:d,minPatternNodeLabelInDegree:s,minPatternNodeLabelOutDegree:u}};const fe=function(e,t,r,n,o,i,a){var d;if(void 0===r&&(r=!1),void 0===i&&(i="cluster"),void 0===a&&(a="cluster"),e&&e.nodes){var s=e.nodes.length;if(s){var u=P(e,r),c=P(t,r),f=se(e.nodes,u,r),h=se(t.nodes,c,r),l=ae(e.nodes,i),v=l.nodeMap,p=l.nodeLabelMap,g=ae(t.nodes,i),b=g.nodeMap,E=g.nodeLabelMap;de(e.edges,a,v);var m=de(t.edges,a,b).edgeLabelMap,L=[];null==c||c.forEach((function(e){L=L.concat(e)})),o||(o=Math.max.apply(Math,y(y([],L,!1),[2],!1))),n||(n=o);var N=re(e,u,i,n),w=re(t,c,i,n),M=function(e,t,r,n,o){var i=Math.ceil(r/t),a={},d=0;return n.forEach((function(e,n){for(var s=0,u=0,c=e.nodeIdxs,f=e.neighborNum-1;s<i;){for(var h=c[1+Math.floor(Math.random()*f)],l=0;(a["".concat(n,"-").concat(h)]||a["".concat(h,"-").concat(n)])&&(h=Math.floor(Math.random()*t),!(++l>2*t)););if(l<2*t&&(a["".concat(n,"-").concat(h)]={start:n,end:h,distance:o[n][h]},s++,++d>=r))return a;if(++u>2*t)break}s<i&&(i=(i+(i-s))/(t-n-1))})),a}(0,s,Math.min(100,s*(s-1)/2),N,u),I=oe(M,N,e),j=function(e){var t=e.graphs,r=e.directed,n=void 0!==r&&r,o=e.nodeLabelProp,i=void 0===o?te:o,a=e.edgeLabelProp,d=void 0===a?te:a,s=function(e,t,r,n){var o={};return Object.keys(e).forEach((function(i,a){var d=e[i],s=new X(a,!0,t),u={};d.nodes.forEach((function(e,t){s.addNode(t,e[r]),u[e.id]=t})),d.edges.forEach((function(e,t){var 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this.dfsEdgeList.forEach((function(t){e[t.fromNode]||(e[t.fromNode]=!0),e[t.toNode]||(e[t.toNode]=!0)})),Object.keys(e).length},e}(),$=function(){function e(e){if(this.his={},this.nodesUsed={},this.edgesUsed={},this.edges=[],e){for(;e;){var t=e.edge;this.edges.push(t),this.nodesUsed[t.from]=1,this.nodesUsed[t.to]=1,this.edgesUsed[t.id]=1,e=e.preNode}this.edges=this.edges.reverse()}}return e.prototype.hasNode=function(e){return 1===this.nodesUsed[e.id]},e.prototype.hasEdge=function(e){return 1===this.edgesUsed[e.id]},e}(),ee=function(){function e(e){var t=e.graphs,r=e.minSupport,n=void 0===r?2:r,o=e.minNodeNum,i=void 0===o?1:o,a=e.maxNodeNum,d=void 0===a?4:a,s=e.top,u=void 0===s?10:s,c=e.directed,f=void 0!==c&&c,h=e.verbose,l=void 0!==h&&h;this.graphs=t,this.dfsCode=new 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r=this.directed,n=t.toGraph(-1,r),o=n.nodeMap,i=new Z,a={};n.nodes.forEach((function(t){e.findForwardRootEdges(n,t).forEach((function(e){var r=o[e.to],i="".concat(t.label,"-").concat(e.label,"-").concat(r.label);a[i]||(a[i]={projected:[],nodeLabel1:t.label,edgeLabel:e.label,nodeLabel2:r.label});var d={graphId:n.id,edge:e,preNode:null};a[i].projected.push(d)}))}));var d=this.findMinLabel(a);if(d){i.dfsEdgeList.push(new Y(0,1,d.nodeLabel1,d.edgeLabel,d.nodeLabel2));var s=function(a){for(var d=i.buildRmpath(),u=i.dfsEdgeList[0].nodeEdgeNodeLabel.nodeLabel1,c=i.dfsEdgeList[d[0]].toNode,f={},h=!1,l=0,v=r?-1:0,p=function(t){if(h)return"break";a.forEach((function(r){var o=new $(r),a=e.findBackwardEdge(n,o.edges[d[t]],o.edges[d[0]],o);a&&(f[a.label]||(f[a.label]={projected:[],edgeLabel:a.label}),f[a.label].projected.push({graphId:n.id,edge:f,preNode:r}),l=i.dfsEdgeList[d[t]].fromNode,h=!0)}))},g=d.length-1;g>v&&"break"!==p(g);g--);if(h){var b=e.findMinLabel(f);i.dfsEdgeList.push(new Y(c,l,V,b.edgeLabel,V));var E=i.dfsEdgeList.length-1;return e.dfsCode.dfsEdgeList[E]===i.dfsEdgeList[E]&&s(f[b.edgeLabel].projected)}var y={};h=!1;var m=0;a.forEach((function(t){var r=new $(t),i=e.findForwardPureEdges(n,r.edges[d[0]],u,r);i.length>0&&(h=!0,m=c,i.forEach((function(e){var r="".concat(e.label,"-").concat(o[e.to].label);y[r]||(y[r]={projected:[],edgeLabel:e.label,nodeLabel2:o[e.to].label}),y[r].projected.push({graphId:n.id,edge:e,preNode:t})})))}));var L=d.length,N=function(t){if(h)return"break";var r=d[t];a.forEach((function(t){var a=new $(t),d=e.findForwardRmpathEdges(n,a.edges[r],u,a);d.length>0&&(h=!0,m=i.dfsEdgeList[r].fromNode,d.forEach((function(e){var r="".concat(e.label,"-").concat(o[e.to].label);y[r]||(y[r]={projected:[],edgeLabel:e.label,nodeLabel2:o[e.to].label}),y[r].projected.push({graphId:n.id,edge:e,preNode:t})})))}))};for(g=0;g<L&&"break"!==N(g);g++);if(!h)return!0;var w=e.findMinLabel(y);i.dfsEdgeList.push(new Y(m,c+1,V,w.edgeLabel,w.nodeLabel2));var M=i.dfsEdgeList.length-1;return t.dfsEdgeList[M]===i.dfsEdgeList[M]&&s(y["".concat(w.edgeLabel,"-").concat(w.nodeLabel2)].projected)},u="".concat(d.nodeLabel1,"-").concat(d.edgeLabel,"-").concat(d.nodeLabel2);return s(a[u].projected)}},e.prototype.report=function(){if(!(this.dfsCode.getNodeNum()<this.minNodeNum)){this.counter++;var e=this.dfsCode.toGraph(this.counter,this.directed);this.frequentSubgraphs.push(j(e))}},e.prototype.subGraphMining=function(e){var t=this;if(!(this.getSupport(e)<this.minSupport)&&this.isMin()){this.report();var r=this.dfsCode.getNodeNum(),n=this.dfsCode.buildRmpath(),o=this.dfsCode.dfsEdgeList[n[0]].toNode,i=this.dfsCode.dfsEdgeList[0].nodeEdgeNodeLabel.nodeLabel1,a={},d={};e.forEach((function(e){for(var s=t.graphs[e.graphId],u=s.nodeMap,c=new $(e),f=n.length-1;f>=0;f--){var h=t.findBackwardEdge(s,c.edges[n[f]],c.edges[n[0]],c);if(h){var l="".concat(t.dfsCode.dfsEdgeList[n[f]].fromNode,"-").concat(h.label);d[l]||(d[l]={projected:[],toNodeId:t.dfsCode.dfsEdgeList[n[f]].fromNode,edgeLabel:h.label}),d[l].projected.push({graphId:e.graphId,edge:h,preNode:e})}}if(!(r>=t.maxNodeNum)){t.findForwardPureEdges(s,c.edges[n[0]],i,c).forEach((function(t){var r="".concat(o,"-").concat(t.label,"-").concat(u[t.to].label);a[r]||(a[r]={projected:[],fromNodeId:o,edgeLabel:t.label,nodeLabel2:u[t.to].label}),a[r].projected.push({graphId:e.graphId,edge:t,preNode:e})}));var v=function(r){t.findForwardRmpathEdges(s,c.edges[n[r]],i,c).forEach((function(o){var i="".concat(t.dfsCode.dfsEdgeList[n[r]].fromNode,"-").concat(o.label,"-").concat(u[o.to].label);a[i]||(a[i]={projected:[],fromNodeId:t.dfsCode.dfsEdgeList[n[r]].fromNode,edgeLabel:o.label,nodeLabel2:u[o.to].label}),a[i].projected.push({graphId:e.graphId,edge:o,preNode:e})}))};for(f=0;f<n.length;f++)v(f)}})),Object.keys(d).forEach((function(e){var r=d[e],n=r.toNodeId,i=r.edgeLabel;t.dfsCode.dfsEdgeList.push(new Y(o,n,"-1",i,"-1")),t.subGraphMining(d[e].projected),t.dfsCode.dfsEdgeList.pop()})),Object.keys(a).forEach((function(e){var r=a[e],n=r.fromNodeId,i=r.edgeLabel,d=r.nodeLabel2;t.dfsCode.dfsEdgeList.push(new Y(n,o+1,V,i,d)),t.subGraphMining(a[e].projected),t.dfsCode.dfsEdgeList.pop()}))}},e.prototype.generate1EdgeFrequentSubGraphs=function(){var e=this.graphs,t=this.directed,r=this.minSupport,n=this.frequentSize1Subgraphs,o={},i={},a={},d={};return Object.keys(e).forEach((function(r){var n=e[r],s=n.nodeMap;n.nodes.forEach((function(e,n){var u=e.label,c="".concat(r,"-").concat(u);if(!a[c]){var f=o[u]||0;f++,o[u]=f}a[c]={graphKey:r,label:u},e.edges.forEach((function(e){var n=u,o=s[e.to].label;if(!t&&n>o){var a=o;o=n,n=a}var c=e.label,f="".concat(r,"-").concat(n,"-").concat(c,"-").concat(o),h="".concat(n,"-").concat(c,"-").concat(o);if(!i[h]){var l=i[h]||0;l++,i[h]=l}d[f]={graphId:r,nodeLabel1:n,edgeLabel:c,nodeLabel2:o}}))}))})),Object.keys(o).forEach((function(e){if(!(o[e]<r)){var t={nodes:[],edges:[]};t.nodes.push({id:"0",label:e}),n.push(t)}})),n},e.prototype.run=function(){var e=this;if(this.frequentSize1Subgraphs=this.generate1EdgeFrequentSubGraphs(),!(this.maxNodeNum<2)){var t=this.graphs,r=(this.directed,{});Object.keys(t).forEach((function(n){var o=t[n],i=o.nodeMap;o.nodes.forEach((function(t){e.findForwardRootEdges(o,t).forEach((function(e){var o=i[e.to],a="".concat(t.label,"-").concat(e.label,"-").concat(o.label);r[a]||(r[a]={projected:[],nodeLabel1:t.label,edgeLabel:e.label,nodeLabel2:o.label});var d={graphId:n,edge:e,preNode:null};r[a].projected.push(d)}))}))})),Object.keys(r).forEach((function(t){var n=r[t],o=n.projected,i=n.nodeLabel1,a=n.edgeLabel,d=n.nodeLabel2;e.dfsCode.dfsEdgeList.push(new Y(0,1,i,a,d)),e.subGraphMining(o),e.dfsCode.dfsEdgeList.pop()}))}},e}(),te="cluster";var re=function(e,t,r,n){void 0===r&&(r="cluster"),void 0===n&&(n=2);var o=[],i=e.nodes;return t.forEach((function(e,t){o.push(ne(i,e,t,r,n))})),o},ne=function(e,t,r,n,o){var i=[r],a=[],d={};return t.forEach((function(t,s){if(t<=o&&r!==s){i.push(s),a.push(e[s]);var u=e[s][n];d[u]?(d[u].count++,d[u].dists.push(t)):d[u]={count:1,dists:[t]}}})),Object.keys(d).forEach((function(e){d[e].dists=d[e].dists.sort((function(e,t){return e-t}))})),{nodeIdx:r,nodeId:e[r].id,nodeIdxs:i,neighbors:a,neighborNum:i.length-1,nodeLabelCountMap:d}},oe=function(e,t,r,n){var o=r.nodes;return n||(n={}),Object.keys(e).forEach((function(i){var a,d;if(!n||!n[i]){n[i]={nodes:[],edges:[]};var s=e[i],u=null===(a=t[s.start])||void 0===a?void 0:a.nodeIdxs,c=null===(d=t[s.end])||void 0===d?void 0:d.nodeIdxs;if(u&&c){var f=new Set(c),h=u.filter((function(e){return f.has(e)}));if(h&&h.length){for(var l={},v=h.length,p=0;p<v;p++){var 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t.edges.forEach((function(e){(r.directed||t.label<=o[e.to].label)&&n.push(e)})),n},e.prototype.findBackwardEdge=function(e,t,r,n){if(!this.directed&&t===r)return null;for(var o=e.nodeMap,i=o[r.to].edges,a=i.length,d=0;d<a;d++){var u=i[d];if(!n.hasEdge(u)&&u.to===t.from)if(this.directed){if(o[t.from].label<o[r.to].label||o[t.from].label===o[r.to].label&&t.label<=u.label)return u}else if(t.label<u.label||t.label===u.label&&o[t.to].label<=o[r.to].label)return u}return null},e.prototype.findForwardPureEdges=function(e,t,r,n){for(var o=[],i=t.to,a=e.nodeMap[i].edges,d=a.length,u=0;u<d;u++){var s=a[u],c=e.nodeMap[s.to];r<=c.label&&!n.hasNode(c)&&o.push(s)}return o},e.prototype.findForwardRmpathEdges=function(e,t,r,n){for(var o=[],i=e.nodeMap,a=i[t.to].label,d=i[t.from].edges,u=d.length,s=0;s<u;s++){var c=d[s],f=i[c.to].label;t.to===c.to||r>f||n.hasNode(i[c.to])||(t.label<c.label||t.label===c.label&&a<=f)&&o.push(c)}return o},e.prototype.getSupport=function(e){var t={};return e.forEach((function(e){t[e.graphId]||(t[e.graphId]=!0)})),Object.keys(t).length},e.prototype.findMinLabel=function(e){var t=void 0;return Object.keys(e).forEach((function(r){var n=e[r],o=n.nodeLabel1,i=n.edgeLabel,a=n.nodeLabel2;t?(o<t.nodeLabel1||o===t.nodeLabel1&&i<t.edgeLabel||o===t.nodeLabel1&&i===t.edgeLabel&&a<t.nodeLabel2)&&(t={nodeLabel1:o,edgeLabel:i,nodeLabel2:a}):t={nodeLabel1:o,edgeLabel:i,nodeLabel2:a}})),t},e.prototype.isMin=function(){var e=this,t=this.dfsCode;if(this.verbose&&console.log("isMin checking",t),1===t.dfsEdgeList.length)return!0;var r=this.directed,n=t.toGraph(-1,r),o=n.nodeMap,i=new Y,a={};n.nodes.forEach((function(t){e.findForwardRootEdges(n,t).forEach((function(e){var r=o[e.to],i="".concat(t.label,"-").concat(e.label,"-").concat(r.label);a[i]||(a[i]={projected:[],nodeLabel1:t.label,edgeLabel:e.label,nodeLabel2:r.label});var d={graphId:n.id,edge:e,preNode:null};a[i].projected.push(d)}))}));var d=this.findMinLabel(a);if(d){i.dfsEdgeList.push(new 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r="".concat(e.label,"-").concat(o[e.to].label);y[r]||(y[r]={projected:[],edgeLabel:e.label,nodeLabel2:o[e.to].label}),y[r].projected.push({graphId:n.id,edge:e,preNode:t})})))}));var L=d.length,N=function(t){if(l)return"break";var r=d[t];a.forEach((function(t){var a=new Z(t),d=e.findForwardRmpathEdges(n,a.edges[r],s,a);d.length>0&&(l=!0,m=i.dfsEdgeList[r].fromNode,d.forEach((function(e){var r="".concat(e.label,"-").concat(o[e.to].label);y[r]||(y[r]={projected:[],edgeLabel:e.label,nodeLabel2:o[e.to].label}),y[r].projected.push({graphId:n.id,edge:e,preNode:t})})))}))};for(g=0;g<L&&"break"!==N(g);g++);if(!l)return!0;var w=e.findMinLabel(y);i.dfsEdgeList.push(new X(m,c+1,K,w.edgeLabel,w.nodeLabel2));var k=i.dfsEdgeList.length-1;return t.dfsEdgeList[k]===i.dfsEdgeList[k]&&u(y["".concat(w.edgeLabel,"-").concat(w.nodeLabel2)].projected)},s="".concat(d.nodeLabel1,"-").concat(d.edgeLabel,"-").concat(d.nodeLabel2);return u(a[s].projected)}},e.prototype.report=function(){if(!(this.dfsCode.getNodeNum()<this.minNodeNum)){this.counter++;var e=this.dfsCode.toGraph(this.counter,this.directed);this.frequentSubgraphs.push(x(e))}},e.prototype.subGraphMining=function(e){var t=this;if(!(this.getSupport(e)<this.minSupport)&&this.isMin()){this.report();var r=this.dfsCode.getNodeNum(),n=this.dfsCode.buildRmpath(),o=this.dfsCode.dfsEdgeList[n[0]].toNode,i=this.dfsCode.dfsEdgeList[0].nodeEdgeNodeLabel.nodeLabel1,a={},d={};e.forEach((function(e){for(var u=t.graphs[e.graphId],s=u.nodeMap,c=new Z(e),f=n.length-1;f>=0;f--){var l=t.findBackwardEdge(u,c.edges[n[f]],c.edges[n[0]],c);if(l){var h="".concat(t.dfsCode.dfsEdgeList[n[f]].fromNode,"-").concat(l.label);d[h]||(d[h]={projected:[],toNodeId:t.dfsCode.dfsEdgeList[n[f]].fromNode,edgeLabel:l.label}),d[h].projected.push({graphId:e.graphId,edge:l,preNode:e})}}if(!(r>=t.maxNodeNum)){t.findForwardPureEdges(u,c.edges[n[0]],i,c).forEach((function(t){var r="".concat(o,"-").concat(t.label,"-").concat(s[t.to].label);a[r]||(a[r]={projected:[],fromNodeId:o,edgeLabel:t.label,nodeLabel2:s[t.to].label}),a[r].projected.push({graphId:e.graphId,edge:t,preNode:e})}));var v=function(r){t.findForwardRmpathEdges(u,c.edges[n[r]],i,c).forEach((function(o){var i="".concat(t.dfsCode.dfsEdgeList[n[r]].fromNode,"-").concat(o.label,"-").concat(s[o.to].label);a[i]||(a[i]={projected:[],fromNodeId:t.dfsCode.dfsEdgeList[n[r]].fromNode,edgeLabel:o.label,nodeLabel2:s[o.to].label}),a[i].projected.push({graphId:e.graphId,edge:o,preNode:e})}))};for(f=0;f<n.length;f++)v(f)}})),Object.keys(d).forEach((function(e){var r=d[e],n=r.toNodeId,i=r.edgeLabel;t.dfsCode.dfsEdgeList.push(new X(o,n,"-1",i,"-1")),t.subGraphMining(d[e].projected),t.dfsCode.dfsEdgeList.pop()})),Object.keys(a).forEach((function(e){var r=a[e],n=r.fromNodeId,i=r.edgeLabel,d=r.nodeLabel2;t.dfsCode.dfsEdgeList.push(new X(n,o+1,K,i,d)),t.subGraphMining(a[e].projected),t.dfsCode.dfsEdgeList.pop()}))}},e.prototype.generate1EdgeFrequentSubGraphs=function(){var e=this.graphs,t=this.directed,r=this.minSupport,n=this.frequentSize1Subgraphs,o={},i={},a={},d={};return Object.keys(e).forEach((function(r){var n=e[r],u=n.nodeMap;n.nodes.forEach((function(e,n){var s=e.label,c="".concat(r,"-").concat(s);if(!a[c]){var f=o[s]||0;f++,o[s]=f}a[c]={graphKey:r,label:s},e.edges.forEach((function(e){var n=s,o=u[e.to].label;if(!t&&n>o){var a=o;o=n,n=a}var c=e.label,f="".concat(r,"-").concat(n,"-").concat(c,"-").concat(o),l="".concat(n,"-").concat(c,"-").concat(o);if(!i[l]){var h=i[l]||0;h++,i[l]=h}d[f]={graphId:r,nodeLabel1:n,edgeLabel:c,nodeLabel2:o}}))}))})),Object.keys(o).forEach((function(e){if(!(o[e]<r)){var t={nodes:[],edges:[]};t.nodes.push({id:"0",label:e}),n.push(t)}})),n},e.prototype.run=function(){var e=this;if(this.frequentSize1Subgraphs=this.generate1EdgeFrequentSubGraphs(),!(this.maxNodeNum<2)){var t=this.graphs,r=(this.directed,{});Object.keys(t).forEach((function(n){var o=t[n],i=o.nodeMap;o.nodes.forEach((function(t){e.findForwardRootEdges(o,t).forEach((function(e){var o=i[e.to],a="".concat(t.label,"-").concat(e.label,"-").concat(o.label);r[a]||(r[a]={projected:[],nodeLabel1:t.label,edgeLabel:e.label,nodeLabel2:o.label});var d={graphId:n,edge:e,preNode:null};r[a].projected.push(d)}))}))})),Object.keys(r).forEach((function(t){var n=r[t],o=n.projected,i=n.nodeLabel1,a=n.edgeLabel,d=n.nodeLabel2;e.dfsCode.dfsEdgeList.push(new X(0,1,i,a,d)),e.subGraphMining(o),e.dfsCode.dfsEdgeList.pop()}))}},e}(),_="cluster";var ee=function(e,t,r,n){void 0===r&&(r="cluster"),void 0===n&&(n=2);var o=[],i=e.nodes;return t.forEach((function(e,t){o.push(te(i,e,t,r,n))})),o},te=function(e,t,r,n,o){var i=[r],a=[],d={};return t.forEach((function(t,u){if(t<=o&&r!==u){i.push(u),a.push(e[u]);var 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u=i[d];if(!n.hasEdge(u)&&u.to===t.from)if(this.directed){if(o[t.from].label<o[r.to].label||o[t.from].label===o[r.to].label&&t.label<=u.label)return u}else if(t.label<u.label||t.label===u.label&&o[t.to].label<=o[r.to].label)return u}return null},e.prototype.findForwardPureEdges=function(e,t,r,n){for(var o=[],i=t.to,a=e.nodeMap[i].edges,d=a.length,u=0;u<d;u++){var s=a[u],c=e.nodeMap[s.to];r<=c.label&&!n.hasNode(c)&&o.push(s)}return o},e.prototype.findForwardRmpathEdges=function(e,t,r,n){for(var o=[],i=e.nodeMap,a=i[t.to].label,d=i[t.from].edges,u=d.length,s=0;s<u;s++){var c=d[s],f=i[c.to].label;t.to===c.to||r>f||n.hasNode(i[c.to])||(t.label<c.label||t.label===c.label&&a<=f)&&o.push(c)}return o},e.prototype.getSupport=function(e){var t={};return e.forEach((function(e){t[e.graphId]||(t[e.graphId]=!0)})),Object.keys(t).length},e.prototype.findMinLabel=function(e){var t=void 0;return Object.keys(e).forEach((function(r){var n=e[r],o=n.nodeLabel1,i=n.edgeLabel,a=n.nodeLabel2;t?(o<t.nodeLabel1||o===t.nodeLabel1&&i<t.edgeLabel||o===t.nodeLabel1&&i===t.edgeLabel&&a<t.nodeLabel2)&&(t={nodeLabel1:o,edgeLabel:i,nodeLabel2:a}):t={nodeLabel1:o,edgeLabel:i,nodeLabel2:a}})),t},e.prototype.isMin=function(){var e=this,t=this.dfsCode;if(this.verbose&&console.log("isMin checking",t),1===t.dfsEdgeList.length)return!0;var r=this.directed,n=t.toGraph(-1,r),o=n.nodeMap,i=new te,a={};n.nodes.forEach((function(t){e.findForwardRootEdges(n,t).forEach((function(e){var r=o[e.to],i="".concat(t.label,"-").concat(e.label,"-").concat(r.label);a[i]||(a[i]={projected:[],nodeLabel1:t.label,edgeLabel:e.label,nodeLabel2:r.label});var d={graphId:n.id,edge:e,preNode:null};a[i].projected.push(d)}))}));var d=this.findMinLabel(a);if(d){i.dfsEdgeList.push(new ee(0,1,d.nodeLabel1,d.edgeLabel,d.nodeLabel2));var u=function(a){for(var d=i.buildRmpath(),s=i.dfsEdgeList[0].nodeEdgeNodeLabel.nodeLabel1,c=i.dfsEdgeList[d[0]].toNode,f={},l=!1,h=0,v=r?-1:0,p=function(t){if(l)return"break";a.forEach((function(r){var o=new re(r),a=e.findBackwardEdge(n,o.edges[d[t]],o.edges[d[0]],o);a&&(f[a.label]||(f[a.label]={projected:[],edgeLabel:a.label}),f[a.label].projected.push({graphId:n.id,edge:f,preNode:r}),h=i.dfsEdgeList[d[t]].fromNode,l=!0)}))},g=d.length-1;g>v&&"break"!==p(g);g--);if(l){var b=e.findMinLabel(f);i.dfsEdgeList.push(new ee(c,h,Y,b.edgeLabel,Y));var E=i.dfsEdgeList.length-1;return e.dfsCode.dfsEdgeList[E]===i.dfsEdgeList[E]&&u(f[b.edgeLabel].projected)}var y={};l=!1;var m=0;a.forEach((function(t){var r=new re(t),i=e.findForwardPureEdges(n,r.edges[d[0]],s,r);i.length>0&&(l=!0,m=c,i.forEach((function(e){var r="".concat(e.label,"-").concat(o[e.to].label);y[r]||(y[r]={projected:[],edgeLabel:e.label,nodeLabel2:o[e.to].label}),y[r].projected.push({graphId:n.id,edge:e,preNode:t})})))}));var L=d.length,N=function(t){if(l)return"break";var r=d[t];a.forEach((function(t){var a=new re(t),d=e.findForwardRmpathEdges(n,a.edges[r],s,a);d.length>0&&(l=!0,m=i.dfsEdgeList[r].fromNode,d.forEach((function(e){var r="".concat(e.label,"-").concat(o[e.to].label);y[r]||(y[r]={projected:[],edgeLabel:e.label,nodeLabel2:o[e.to].label}),y[r].projected.push({graphId:n.id,edge:e,preNode:t})})))}))};for(g=0;g<L&&"break"!==N(g);g++);if(!l)return!0;var w=e.findMinLabel(y);i.dfsEdgeList.push(new ee(m,c+1,Y,w.edgeLabel,w.nodeLabel2));var k=i.dfsEdgeList.length-1;return t.dfsEdgeList[k]===i.dfsEdgeList[k]&&u(y["".concat(w.edgeLabel,"-").concat(w.nodeLabel2)].projected)},s="".concat(d.nodeLabel1,"-").concat(d.edgeLabel,"-").concat(d.nodeLabel2);return u(a[s].projected)}},e.prototype.report=function(){if(!(this.dfsCode.getNodeNum()<this.minNodeNum)){this.counter++;var e=this.dfsCode.toGraph(this.counter,this.directed);this.frequentSubgraphs.push(j(e))}},e.prototype.subGraphMining=function(e){var t=this;if(!(this.getSupport(e)<this.minSupport)&&this.isMin()){this.report();var r=this.dfsCode.getNodeNum(),n=this.dfsCode.buildRmpath(),o=this.dfsCode.dfsEdgeList[n[0]].toNode,i=this.dfsCode.dfsEdgeList[0].nodeEdgeNodeLabel.nodeLabel1,a={},d={};e.forEach((function(e){for(var u=t.graphs[e.graphId],s=u.nodeMap,c=new re(e),f=n.length-1;f>=0;f--){var l=t.findBackwardEdge(u,c.edges[n[f]],c.edges[n[0]],c);if(l){var h="".concat(t.dfsCode.dfsEdgeList[n[f]].fromNode,"-").concat(l.label);d[h]||(d[h]={projected:[],toNodeId:t.dfsCode.dfsEdgeList[n[f]].fromNode,edgeLabel:l.label}),d[h].projected.push({graphId:e.graphId,edge:l,preNode:e})}}if(!(r>=t.maxNodeNum)){t.findForwardPureEdges(u,c.edges[n[0]],i,c).forEach((function(t){var r="".concat(o,"-").concat(t.label,"-").concat(s[t.to].label);a[r]||(a[r]={projected:[],fromNodeId:o,edgeLabel:t.label,nodeLabel2:s[t.to].label}),a[r].projected.push({graphId:e.graphId,edge:t,preNode:e})}));var v=function(r){t.findForwardRmpathEdges(u,c.edges[n[r]],i,c).forEach((function(o){var i="".concat(t.dfsCode.dfsEdgeList[n[r]].fromNode,"-").concat(o.label,"-").concat(s[o.to].label);a[i]||(a[i]={projected:[],fromNodeId:t.dfsCode.dfsEdgeList[n[r]].fromNode,edgeLabel:o.label,nodeLabel2:s[o.to].label}),a[i].projected.push({graphId:e.graphId,edge:o,preNode:e})}))};for(f=0;f<n.length;f++)v(f)}})),Object.keys(d).forEach((function(e){var r=d[e],n=r.toNodeId,i=r.edgeLabel;t.dfsCode.dfsEdgeList.push(new ee(o,n,"-1",i,"-1")),t.subGraphMining(d[e].projected),t.dfsCode.dfsEdgeList.pop()})),Object.keys(a).forEach((function(e){var r=a[e],n=r.fromNodeId,i=r.edgeLabel,d=r.nodeLabel2;t.dfsCode.dfsEdgeList.push(new ee(n,o+1,Y,i,d)),t.subGraphMining(a[e].projected),t.dfsCode.dfsEdgeList.pop()}))}},e.prototype.generate1EdgeFrequentSubGraphs=function(){var e=this.graphs,t=this.directed,r=this.minSupport,n=this.frequentSize1Subgraphs,o={},i={},a={},d={};return Object.keys(e).forEach((function(r){var n=e[r],u=n.nodeMap;n.nodes.forEach((function(e,n){var s=e.label,c="".concat(r,"-").concat(s);if(!a[c]){var f=o[s]||0;f++,o[s]=f}a[c]={graphKey:r,label:s},e.edges.forEach((function(e){var n=s,o=u[e.to].label;if(!t&&n>o){var a=o;o=n,n=a}var c=e.label,f="".concat(r,"-").concat(n,"-").concat(c,"-").concat(o),l="".concat(n,"-").concat(c,"-").concat(o);if(!i[l]){var h=i[l]||0;h++,i[l]=h}d[f]={graphId:r,nodeLabel1:n,edgeLabel:c,nodeLabel2:o}}))}))})),Object.keys(o).forEach((function(e){if(!(o[e]<r)){var t={nodes:[],edges:[]};t.nodes.push({id:"0",label:e}),n.push(t)}})),n},e.prototype.run=function(){var e=this;if(this.frequentSize1Subgraphs=this.generate1EdgeFrequentSubGraphs(),!(this.maxNodeNum<2)){var t=this.graphs,r=(this.directed,{});Object.keys(t).forEach((function(n){var o=t[n],i=o.nodeMap;o.nodes.forEach((function(t){e.findForwardRootEdges(o,t).forEach((function(e){var o=i[e.to],a="".concat(t.label,"-").concat(e.label,"-").concat(o.label);r[a]||(r[a]={projected:[],nodeLabel1:t.label,edgeLabel:e.label,nodeLabel2:o.label});var d={graphId:n,edge:e,preNode:null};r[a].projected.push(d)}))}))})),Object.keys(r).forEach((function(t){var n=r[t],o=n.projected,i=n.nodeLabel1,a=n.edgeLabel,d=n.nodeLabel2;e.dfsCode.dfsEdgeList.push(new ee(0,1,i,a,d)),e.subGraphMining(o),e.dfsCode.dfsEdgeList.pop()}))}},e}(),oe="cluster";var ie=function(e,t,r,n){void 0===r&&(r="cluster"),void 0===n&&(n=2);var o=[],i=e.nodes;return t.forEach((function(e,t){o.push(ae(i,e,t,r,n))})),o},ae=function(e,t,r,n,o){var i=[r],a=[],d={};return t.forEach((function(t,u){if(t<=o&&r!==u){i.push(u),a.push(e[u]);var s=e[u][n];d[s]?(d[s].count++,d[s].dists.push(t)):d[s]={count:1,dists:[t]}}})),Object.keys(d).forEach((function(e){d[e].dists=d[e].dists.sort((function(e,t){return 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2*e+1},e.prototype.getRight=function(e){return 2*e+2},e.prototype.getParent=function(e){return 0===e?null:Math.floor((e-1)/2)},e.prototype.isEmpty=function(){return this.list.length<=0},e.prototype.top=function(){return this.isEmpty()?void 0:this.list[0]},e.prototype.delMin=function(){var e=this.top(),t=this.list.pop();return this.list.length>0&&(this.list[0]=t,this.moveDown(0)),e},e.prototype.insert=function(e){if(null!==e){this.list.push(e);var t=this.list.length-1;return this.moveUp(t),!0}return!1},e.prototype.moveUp=function(e){for(var t=this.getParent(e);e&&e>0&&this.compareFn(this.list[t],this.list[e])>0;){var r=this.list[t];this.list[t]=this.list[e],this.list[e]=r,e=t,t=this.getParent(e)}},e.prototype.moveDown=function(e){var t,r=e,n=this.getLeft(e),o=this.getRight(e),i=this.list.length;null!==n&&n<i&&this.compareFn(this.list[r],this.list[n])>0?r=n:null!==o&&o<i&&this.compareFn(this.list[r],this.list[o])>0&&(r=o),e!==r&&(t=[this.list[r],this.list[e]],this.list[e]=t[0],this.list[r]=t[1],this.moveDown(r))},e}();var B=function(e,t){var r=[],n=e.nodes,o=void 0===n?[]:n,i=e.edges,a=void 0===i?[]:i;if(0===o.length)return r;var d=o[0],u=new Set;u.add(d);var c=new _((function(e,r){return t?e.weight-r.weight:0}));for(s(d.id,a).forEach((function(e){c.insert(e)}));!c.isEmpty();){var f=c.delMin(),h=f.source,l=f.target;u.has(h)&&u.has(l)||(r.push(f),u.has(h)||(u.add(h),s(h,a).forEach((function(e){c.insert(e)}))),u.has(l)||(u.add(l),s(l,a).forEach((function(e){c.insert(e)}))))}return r},H=function(e,t){var r=[],n=e.nodes,o=void 0===n?[]:n,i=e.edges,a=void 0===i?[]:i;if(0===o.length)return r;var d=a.map((function(e){return e}));t&&d.sort((function(e,t){return e.weight-t.weight}));for(var s=new G(o.map((function(e){return 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e.prototype.addEdge=function(e){this.edges.push(e),this.edgeMap[e.id]=e},e}(),X=function(){function e(e,t,r){void 0===e&&(e=-1),void 0===t&&(t=!0),void 0===r&&(r=!1),this.id=e,this.edgeIdAutoIncrease=t,this.edges=[],this.nodes=[],this.nodeMap={},this.edgeMap={},this.nodeLabelMap={},this.edgeLabelMap={},this.counter=0,this.directed=r}return e.prototype.getNodeNum=function(){return this.nodes.length},e.prototype.addNode=function(e,t){if(!this.nodeMap[e]){var r=new Q(e,t);this.nodes.push(r),this.nodeMap[e]=r,this.nodeLabelMap[t]||(this.nodeLabelMap[t]=[]),this.nodeLabelMap[t].push(e)}},e.prototype.addEdge=function(e,t,r,n){if((this.edgeIdAutoIncrease||void 0===e)&&(e=this.counter++),!(this.nodeMap[t]&&this.nodeMap[r]&&this.nodeMap[r].edgeMap[e])){var o=new J(e,t,r,n);if(this.edges.push(o),this.edgeMap[e]=o,this.nodeMap[t].addEdge(o),this.edgeLabelMap[n]||(this.edgeLabelMap[n]=[]),this.edgeLabelMap[n].push(o),!this.directed){var i=new J(e,r,t,n);this.nodeMap[r].addEdge(i),this.edgeLabelMap[n].push(i)}}},e}(),Y=function(){function e(e,t,r,n,o){this.fromNode=e,this.toNode=t,this.nodeEdgeNodeLabel={nodeLabel1:r||V,edgeLabel:n||"-1",nodeLabel2:o||V}}return e.prototype.equalTo=function(e){return this.fromNode===e.formNode&&this.toNode===e.toNode&&this.nodeEdgeNodeLabel===e.nodeEdgeNodeLabel},e.prototype.notEqualTo=function(e){return!this.equalTo(e)},e}(),Z=function(){function e(){this.rmpath=[],this.dfsEdgeList=[]}return e.prototype.equalTo=function(e){var t=this.dfsEdgeList.length;if(t!==e.length)return!1;for(var r=0;r<t;r++)if(this.dfsEdgeList[r]!==e[r])return!1;return!0},e.prototype.notEqualTo=function(e){return!this.equalTo(e)},e.prototype.pushBack=function(e,t,r,n,o){return this.dfsEdgeList.push(new Y(e,t,r,n,o)),this.dfsEdgeList},e.prototype.toGraph=function(e,t){void 0===e&&(e=-1),void 0===t&&(t=!1);var r=new X(e,!0,t);return this.dfsEdgeList.forEach((function(e){var t=e.fromNode,n=e.toNode,o=e.nodeEdgeNodeLabel,i=o.nodeLabel1,a=o.edgeLabel,d=o.nodeLabel2;i!==V&&r.addNode(t,i),d!==V&&r.addNode(n,d),i!==V&&d!==i&&r.addEdge(void 0,t,n,a)})),r},e.prototype.buildRmpath=function(){this.rmpath=[];for(var e=void 0,t=this.dfsEdgeList.length-1;t>=0;t--){var r=this.dfsEdgeList[t],n=r.fromNode,o=r.toNode;n<o&&(void 0===e||o===e)&&(this.rmpath.push(t),e=n)}return this.rmpath},e.prototype.getNodeNum=function(){var e={};return this.dfsEdgeList.forEach((function(t){e[t.fromNode]||(e[t.fromNode]=!0),e[t.toNode]||(e[t.toNode]=!0)})),Object.keys(e).length},e}(),$=function(){function e(e){if(this.his={},this.nodesUsed={},this.edgesUsed={},this.edges=[],e){for(;e;){var t=e.edge;this.edges.push(t),this.nodesUsed[t.from]=1,this.nodesUsed[t.to]=1,this.edgesUsed[t.id]=1,e=e.preNode}this.edges=this.edges.reverse()}}return e.prototype.hasNode=function(e){return 1===this.nodesUsed[e.id]},e.prototype.hasEdge=function(e){return 1===this.edgesUsed[e.id]},e}(),ee=function(){function e(e){var t=e.graphs,r=e.minSupport,n=void 0===r?2:r,o=e.minNodeNum,i=void 0===o?1:o,a=e.maxNodeNum,d=void 0===a?4:a,s=e.top,u=void 0===s?10:s,c=e.directed,f=void 0!==c&&c,h=e.verbose,l=void 0!==h&&h;this.graphs=t,this.dfsCode=new Z,this.support=0,this.frequentSize1Subgraphs=[],this.frequentSubgraphs=[],this.minSupport=n,this.top=u,this.directed=f,this.counter=0,this.maxNodeNum=d,this.minNodeNum=i,this.verbose=l,this.maxNodeNum<this.minNodeNum&&(this.maxNodeNum=this.minNodeNum),this.reportDF=[]}return e.prototype.findForwardRootEdges=function(e,t){var r=this,n=[],o=e.nodeMap;return t.edges.forEach((function(e){(r.directed||t.label<=o[e.to].label)&&n.push(e)})),n},e.prototype.findBackwardEdge=function(e,t,r,n){if(!this.directed&&t===r)return null;for(var o=e.nodeMap,i=o[r.to].edges,a=i.length,d=0;d<a;d++){var s=i[d];if(!n.hasEdge(s)&&s.to===t.from)if(this.directed){if(o[t.from].label<o[r.to].label||o[t.from].label===o[r.to].label&&t.label<=s.label)return s}else if(t.label<s.label||t.label===s.label&&o[t.to].label<=o[r.to].label)return s}return null},e.prototype.findForwardPureEdges=function(e,t,r,n){for(var o=[],i=t.to,a=e.nodeMap[i].edges,d=a.length,s=0;s<d;s++){var u=a[s],c=e.nodeMap[u.to];r<=c.label&&!n.hasNode(c)&&o.push(u)}return o},e.prototype.findForwardRmpathEdges=function(e,t,r,n){for(var o=[],i=e.nodeMap,a=i[t.to].label,d=i[t.from].edges,s=d.length,u=0;u<s;u++){var c=d[u],f=i[c.to].label;t.to===c.to||r>f||n.hasNode(i[c.to])||(t.label<c.label||t.label===c.label&&a<=f)&&o.push(c)}return o},e.prototype.getSupport=function(e){var t={};return e.forEach((function(e){t[e.graphId]||(t[e.graphId]=!0)})),Object.keys(t).length},e.prototype.findMinLabel=function(e){var t=void 0;return Object.keys(e).forEach((function(r){var n=e[r],o=n.nodeLabel1,i=n.edgeLabel,a=n.nodeLabel2;t?(o<t.nodeLabel1||o===t.nodeLabel1&&i<t.edgeLabel||o===t.nodeLabel1&&i===t.edgeLabel&&a<t.nodeLabel2)&&(t={nodeLabel1:o,edgeLabel:i,nodeLabel2:a}):t={nodeLabel1:o,edgeLabel:i,nodeLabel2:a}})),t},e.prototype.isMin=function(){var e=this,t=this.dfsCode;if(this.verbose&&console.log("isMin checking",t),1===t.dfsEdgeList.length)return!0;var r=this.directed,n=t.toGraph(-1,r),o=n.nodeMap,i=new Z,a={};n.nodes.forEach((function(t){e.findForwardRootEdges(n,t).forEach((function(e){var r=o[e.to],i="".concat(t.label,"-").concat(e.label,"-").concat(r.label);a[i]||(a[i]={projected:[],nodeLabel1:t.label,edgeLabel:e.label,nodeLabel2:r.label});var d={graphId:n.id,edge:e,preNode:null};a[i].projected.push(d)}))}));var d=this.findMinLabel(a);if(d){i.dfsEdgeList.push(new Y(0,1,d.nodeLabel1,d.edgeLabel,d.nodeLabel2));var s=function(a){for(var d=i.buildRmpath(),u=i.dfsEdgeList[0].nodeEdgeNodeLabel.nodeLabel1,c=i.dfsEdgeList[d[0]].toNode,f={},h=!1,l=0,v=r?-1:0,p=function(t){if(h)return"break";a.forEach((function(r){var o=new $(r),a=e.findBackwardEdge(n,o.edges[d[t]],o.edges[d[0]],o);a&&(f[a.label]||(f[a.label]={projected:[],edgeLabel:a.label}),f[a.label].projected.push({graphId:n.id,edge:f,preNode:r}),l=i.dfsEdgeList[d[t]].fromNode,h=!0)}))},g=d.length-1;g>v&&"break"!==p(g);g--);if(h){var b=e.findMinLabel(f);i.dfsEdgeList.push(new Y(c,l,V,b.edgeLabel,V));var E=i.dfsEdgeList.length-1;return e.dfsCode.dfsEdgeList[E]===i.dfsEdgeList[E]&&s(f[b.edgeLabel].projected)}var y={};h=!1;var m=0;a.forEach((function(t){var r=new $(t),i=e.findForwardPureEdges(n,r.edges[d[0]],u,r);i.length>0&&(h=!0,m=c,i.forEach((function(e){var r="".concat(e.label,"-").concat(o[e.to].label);y[r]||(y[r]={projected:[],edgeLabel:e.label,nodeLabel2:o[e.to].label}),y[r].projected.push({graphId:n.id,edge:e,preNode:t})})))}));var L=d.length,N=function(t){if(h)return"break";var r=d[t];a.forEach((function(t){var a=new $(t),d=e.findForwardRmpathEdges(n,a.edges[r],u,a);d.length>0&&(h=!0,m=i.dfsEdgeList[r].fromNode,d.forEach((function(e){var r="".concat(e.label,"-").concat(o[e.to].label);y[r]||(y[r]={projected:[],edgeLabel:e.label,nodeLabel2:o[e.to].label}),y[r].projected.push({graphId:n.id,edge:e,preNode:t})})))}))};for(g=0;g<L&&"break"!==N(g);g++);if(!h)return!0;var w=e.findMinLabel(y);i.dfsEdgeList.push(new Y(m,c+1,V,w.edgeLabel,w.nodeLabel2));var M=i.dfsEdgeList.length-1;return t.dfsEdgeList[M]===i.dfsEdgeList[M]&&s(y["".concat(w.edgeLabel,"-").concat(w.nodeLabel2)].projected)},u="".concat(d.nodeLabel1,"-").concat(d.edgeLabel,"-").concat(d.nodeLabel2);return s(a[u].projected)}},e.prototype.report=function(){if(!(this.dfsCode.getNodeNum()<this.minNodeNum)){this.counter++;var e=this.dfsCode.toGraph(this.counter,this.directed);this.frequentSubgraphs.push(j(e))}},e.prototype.subGraphMining=function(e){var t=this;if(!(this.getSupport(e)<this.minSupport)&&this.isMin()){this.report();var r=this.dfsCode.getNodeNum(),n=this.dfsCode.buildRmpath(),o=this.dfsCode.dfsEdgeList[n[0]].toNode,i=this.dfsCode.dfsEdgeList[0].nodeEdgeNodeLabel.nodeLabel1,a={},d={};e.forEach((function(e){for(var s=t.graphs[e.graphId],u=s.nodeMap,c=new $(e),f=n.length-1;f>=0;f--){var h=t.findBackwardEdge(s,c.edges[n[f]],c.edges[n[0]],c);if(h){var l="".concat(t.dfsCode.dfsEdgeList[n[f]].fromNode,"-").concat(h.label);d[l]||(d[l]={projected:[],toNodeId:t.dfsCode.dfsEdgeList[n[f]].fromNode,edgeLabel:h.label}),d[l].projected.push({graphId:e.graphId,edge:h,preNode:e})}}if(!(r>=t.maxNodeNum)){t.findForwardPureEdges(s,c.edges[n[0]],i,c).forEach((function(t){var r="".concat(o,"-").concat(t.label,"-").concat(u[t.to].label);a[r]||(a[r]={projected:[],fromNodeId:o,edgeLabel:t.label,nodeLabel2:u[t.to].label}),a[r].projected.push({graphId:e.graphId,edge:t,preNode:e})}));var v=function(r){t.findForwardRmpathEdges(s,c.edges[n[r]],i,c).forEach((function(o){var i="".concat(t.dfsCode.dfsEdgeList[n[r]].fromNode,"-").concat(o.label,"-").concat(u[o.to].label);a[i]||(a[i]={projected:[],fromNodeId:t.dfsCode.dfsEdgeList[n[r]].fromNode,edgeLabel:o.label,nodeLabel2:u[o.to].label}),a[i].projected.push({graphId:e.graphId,edge:o,preNode:e})}))};for(f=0;f<n.length;f++)v(f)}})),Object.keys(d).forEach((function(e){var r=d[e],n=r.toNodeId,i=r.edgeLabel;t.dfsCode.dfsEdgeList.push(new Y(o,n,"-1",i,"-1")),t.subGraphMining(d[e].projected),t.dfsCode.dfsEdgeList.pop()})),Object.keys(a).forEach((function(e){var r=a[e],n=r.fromNodeId,i=r.edgeLabel,d=r.nodeLabel2;t.dfsCode.dfsEdgeList.push(new Y(n,o+1,V,i,d)),t.subGraphMining(a[e].projected),t.dfsCode.dfsEdgeList.pop()}))}},e.prototype.generate1EdgeFrequentSubGraphs=function(){var e=this.graphs,t=this.directed,r=this.minSupport,n=this.frequentSize1Subgraphs,o={},i={},a={},d={};return Object.keys(e).forEach((function(r){var n=e[r],s=n.nodeMap;n.nodes.forEach((function(e,n){var u=e.label,c="".concat(r,"-").concat(u);if(!a[c]){var f=o[u]||0;f++,o[u]=f}a[c]={graphKey:r,label:u},e.edges.forEach((function(e){var n=u,o=s[e.to].label;if(!t&&n>o){var a=o;o=n,n=a}var c=e.label,f="".concat(r,"-").concat(n,"-").concat(c,"-").concat(o),h="".concat(n,"-").concat(c,"-").concat(o);if(!i[h]){var l=i[h]||0;l++,i[h]=l}d[f]={graphId:r,nodeLabel1:n,edgeLabel:c,nodeLabel2:o}}))}))})),Object.keys(o).forEach((function(e){if(!(o[e]<r)){var t={nodes:[],edges:[]};t.nodes.push({id:"0",label:e}),n.push(t)}})),n},e.prototype.run=function(){var e=this;if(this.frequentSize1Subgraphs=this.generate1EdgeFrequentSubGraphs(),!(this.maxNodeNum<2)){var t=this.graphs,r=(this.directed,{});Object.keys(t).forEach((function(n){var o=t[n],i=o.nodeMap;o.nodes.forEach((function(t){e.findForwardRootEdges(o,t).forEach((function(e){var 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this.find(e)===this.find(t)},e}();var z=function(e,t){return e-t};const _=function(){function e(e){void 0===e&&(e=z),this.compareFn=e,this.list=[]}return e.prototype.getLeft=function(e){return 2*e+1},e.prototype.getRight=function(e){return 2*e+2},e.prototype.getParent=function(e){return 0===e?null:Math.floor((e-1)/2)},e.prototype.isEmpty=function(){return this.list.length<=0},e.prototype.top=function(){return this.isEmpty()?void 0:this.list[0]},e.prototype.delMin=function(){var e=this.top(),t=this.list.pop();return this.list.length>0&&(this.list[0]=t,this.moveDown(0)),e},e.prototype.insert=function(e){if(null!==e){this.list.push(e);var t=this.list.length-1;return this.moveUp(t),!0}return!1},e.prototype.moveUp=function(e){for(var t=this.getParent(e);e&&e>0&&this.compareFn(this.list[t],this.list[e])>0;){var r=this.list[t];this.list[t]=this.list[e],this.list[e]=r,e=t,t=this.getParent(e)}},e.prototype.moveDown=function(e){var t,r=e,n=this.getLeft(e),o=this.getRight(e),i=this.list.length;null!==n&&n<i&&this.compareFn(this.list[r],this.list[n])>0?r=n:null!==o&&o<i&&this.compareFn(this.list[r],this.list[o])>0&&(r=o),e!==r&&(t=[this.list[r],this.list[e]],this.list[e]=t[0],this.list[r]=t[1],this.moveDown(r))},e}();var B=function(e,t){var r=[],n=e.nodes,o=void 0===n?[]:n,i=e.edges,a=void 0===i?[]:i;if(0===o.length)return r;var d=o[0],u=new Set;u.add(d);var c=new _((function(e,r){return t?e.weight-r.weight:0}));for(s(d.id,a).forEach((function(e){c.insert(e)}));!c.isEmpty();){var f=c.delMin(),h=f.source,l=f.target;u.has(h)&&u.has(l)||(r.push(f),u.has(h)||(u.add(h),s(h,a).forEach((function(e){c.insert(e)}))),u.has(l)||(u.add(l),s(l,a).forEach((function(e){c.insert(e)}))))}return r},H=function(e,t){var r=[],n=e.nodes,o=void 0===n?[]:n,i=e.edges,a=void 0===i?[]:i;if(0===o.length)return r;var d=a.map((function(e){return e}));t&&d.sort((function(e,t){return e.weight-t.weight}));for(var s=new G(o.map((function(e){return 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J(e,r,t,n);this.nodeMap[r].addEdge(i),this.edgeLabelMap[n].push(i)}}},e}(),Y=function(){function e(e,t,r,n,o){this.fromNode=e,this.toNode=t,this.nodeEdgeNodeLabel={nodeLabel1:r||V,edgeLabel:n||"-1",nodeLabel2:o||V}}return e.prototype.equalTo=function(e){return this.fromNode===e.formNode&&this.toNode===e.toNode&&this.nodeEdgeNodeLabel===e.nodeEdgeNodeLabel},e.prototype.notEqualTo=function(e){return!this.equalTo(e)},e}(),Z=function(){function e(){this.rmpath=[],this.dfsEdgeList=[]}return e.prototype.equalTo=function(e){var t=this.dfsEdgeList.length;if(t!==e.length)return!1;for(var r=0;r<t;r++)if(this.dfsEdgeList[r]!==e[r])return!1;return!0},e.prototype.notEqualTo=function(e){return!this.equalTo(e)},e.prototype.pushBack=function(e,t,r,n,o){return this.dfsEdgeList.push(new Y(e,t,r,n,o)),this.dfsEdgeList},e.prototype.toGraph=function(e,t){void 0===e&&(e=-1),void 0===t&&(t=!1);var r=new X(e,!0,t);return this.dfsEdgeList.forEach((function(e){var t=e.fromNode,n=e.toNode,o=e.nodeEdgeNodeLabel,i=o.nodeLabel1,a=o.edgeLabel,d=o.nodeLabel2;i!==V&&r.addNode(t,i),d!==V&&r.addNode(n,d),i!==V&&d!==i&&r.addEdge(void 0,t,n,a)})),r},e.prototype.buildRmpath=function(){this.rmpath=[];for(var e=void 0,t=this.dfsEdgeList.length-1;t>=0;t--){var r=this.dfsEdgeList[t],n=r.fromNode,o=r.toNode;n<o&&(void 0===e||o===e)&&(this.rmpath.push(t),e=n)}return this.rmpath},e.prototype.getNodeNum=function(){var e={};return this.dfsEdgeList.forEach((function(t){e[t.fromNode]||(e[t.fromNode]=!0),e[t.toNode]||(e[t.toNode]=!0)})),Object.keys(e).length},e}(),$=function(){function e(e){if(this.his={},this.nodesUsed={},this.edgesUsed={},this.edges=[],e){for(;e;){var t=e.edge;this.edges.push(t),this.nodesUsed[t.from]=1,this.nodesUsed[t.to]=1,this.edgesUsed[t.id]=1,e=e.preNode}this.edges=this.edges.reverse()}}return e.prototype.hasNode=function(e){return 1===this.nodesUsed[e.id]},e.prototype.hasEdge=function(e){return 1===this.edgesUsed[e.id]},e}(),ee=function(){function e(e){var t=e.graphs,r=e.minSupport,n=void 0===r?2:r,o=e.minNodeNum,i=void 0===o?1:o,a=e.maxNodeNum,d=void 0===a?4:a,s=e.top,u=void 0===s?10:s,c=e.directed,f=void 0!==c&&c,h=e.verbose,l=void 0!==h&&h;this.graphs=t,this.dfsCode=new Z,this.support=0,this.frequentSize1Subgraphs=[],this.frequentSubgraphs=[],this.minSupport=n,this.top=u,this.directed=f,this.counter=0,this.maxNodeNum=d,this.minNodeNum=i,this.verbose=l,this.maxNodeNum<this.minNodeNum&&(this.maxNodeNum=this.minNodeNum),this.reportDF=[]}return e.prototype.findForwardRootEdges=function(e,t){var r=this,n=[],o=e.nodeMap;return t.edges.forEach((function(e){(r.directed||t.label<=o[e.to].label)&&n.push(e)})),n},e.prototype.findBackwardEdge=function(e,t,r,n){if(!this.directed&&t===r)return null;for(var o=e.nodeMap,i=o[r.to].edges,a=i.length,d=0;d<a;d++){var s=i[d];if(!n.hasEdge(s)&&s.to===t.from)if(this.directed){if(o[t.from].label<o[r.to].label||o[t.from].label===o[r.to].label&&t.label<=s.label)return s}else if(t.label<s.label||t.label===s.label&&o[t.to].label<=o[r.to].label)return s}return null},e.prototype.findForwardPureEdges=function(e,t,r,n){for(var o=[],i=t.to,a=e.nodeMap[i].edges,d=a.length,s=0;s<d;s++){var u=a[s],c=e.nodeMap[u.to];r<=c.label&&!n.hasNode(c)&&o.push(u)}return o},e.prototype.findForwardRmpathEdges=function(e,t,r,n){for(var o=[],i=e.nodeMap,a=i[t.to].label,d=i[t.from].edges,s=d.length,u=0;u<s;u++){var c=d[u],f=i[c.to].label;t.to===c.to||r>f||n.hasNode(i[c.to])||(t.label<c.label||t.label===c.label&&a<=f)&&o.push(c)}return o},e.prototype.getSupport=function(e){var t={};return e.forEach((function(e){t[e.graphId]||(t[e.graphId]=!0)})),Object.keys(t).length},e.prototype.findMinLabel=function(e){var t=void 0;return Object.keys(e).forEach((function(r){var n=e[r],o=n.nodeLabel1,i=n.edgeLabel,a=n.nodeLabel2;t?(o<t.nodeLabel1||o===t.nodeLabel1&&i<t.edgeLabel||o===t.nodeLabel1&&i===t.edgeLabel&&a<t.nodeLabel2)&&(t={nodeLabel1:o,edgeLabel:i,nodeLabel2:a}):t={nodeLabel1:o,edgeLabel:i,nodeLabel2:a}})),t},e.prototype.isMin=function(){var e=this,t=this.dfsCode;if(this.verbose&&console.log("isMin checking",t),1===t.dfsEdgeList.length)return!0;var r=this.directed,n=t.toGraph(-1,r),o=n.nodeMap,i=new Z,a={};n.nodes.forEach((function(t){e.findForwardRootEdges(n,t).forEach((function(e){var r=o[e.to],i="".concat(t.label,"-").concat(e.label,"-").concat(r.label);a[i]||(a[i]={projected:[],nodeLabel1:t.label,edgeLabel:e.label,nodeLabel2:r.label});var d={graphId:n.id,edge:e,preNode:null};a[i].projected.push(d)}))}));var d=this.findMinLabel(a);if(d){i.dfsEdgeList.push(new Y(0,1,d.nodeLabel1,d.edgeLabel,d.nodeLabel2));var s=function(a){for(var d=i.buildRmpath(),u=i.dfsEdgeList[0].nodeEdgeNodeLabel.nodeLabel1,c=i.dfsEdgeList[d[0]].toNode,f={},h=!1,l=0,v=r?-1:0,p=function(t){if(h)return"break";a.forEach((function(r){var o=new $(r),a=e.findBackwardEdge(n,o.edges[d[t]],o.edges[d[0]],o);a&&(f[a.label]||(f[a.label]={projected:[],edgeLabel:a.label}),f[a.label].projected.push({graphId:n.id,edge:f,preNode:r}),l=i.dfsEdgeList[d[t]].fromNode,h=!0)}))},g=d.length-1;g>v&&"break"!==p(g);g--);if(h){var b=e.findMinLabel(f);i.dfsEdgeList.push(new Y(c,l,V,b.edgeLabel,V));var E=i.dfsEdgeList.length-1;return e.dfsCode.dfsEdgeList[E]===i.dfsEdgeList[E]&&s(f[b.edgeLabel].projected)}var y={};h=!1;var m=0;a.forEach((function(t){var r=new $(t),i=e.findForwardPureEdges(n,r.edges[d[0]],u,r);i.length>0&&(h=!0,m=c,i.forEach((function(e){var r="".concat(e.label,"-").concat(o[e.to].label);y[r]||(y[r]={projected:[],edgeLabel:e.label,nodeLabel2:o[e.to].label}),y[r].projected.push({graphId:n.id,edge:e,preNode:t})})))}));var L=d.length,N=function(t){if(h)return"break";var r=d[t];a.forEach((function(t){var a=new $(t),d=e.findForwardRmpathEdges(n,a.edges[r],u,a);d.length>0&&(h=!0,m=i.dfsEdgeList[r].fromNode,d.forEach((function(e){var r="".concat(e.label,"-").concat(o[e.to].label);y[r]||(y[r]={projected:[],edgeLabel:e.label,nodeLabel2:o[e.to].label}),y[r].projected.push({graphId:n.id,edge:e,preNode:t})})))}))};for(g=0;g<L&&"break"!==N(g);g++);if(!h)return!0;var w=e.findMinLabel(y);i.dfsEdgeList.push(new Y(m,c+1,V,w.edgeLabel,w.nodeLabel2));var M=i.dfsEdgeList.length-1;return t.dfsEdgeList[M]===i.dfsEdgeList[M]&&s(y["".concat(w.edgeLabel,"-").concat(w.nodeLabel2)].projected)},u="".concat(d.nodeLabel1,"-").concat(d.edgeLabel,"-").concat(d.nodeLabel2);return s(a[u].projected)}},e.prototype.report=function(){if(!(this.dfsCode.getNodeNum()<this.minNodeNum)){this.counter++;var e=this.dfsCode.toGraph(this.counter,this.directed);this.frequentSubgraphs.push(j(e))}},e.prototype.subGraphMining=function(e){var t=this;if(!(this.getSupport(e)<this.minSupport)&&this.isMin()){this.report();var r=this.dfsCode.getNodeNum(),n=this.dfsCode.buildRmpath(),o=this.dfsCode.dfsEdgeList[n[0]].toNode,i=this.dfsCode.dfsEdgeList[0].nodeEdgeNodeLabel.nodeLabel1,a={},d={};e.forEach((function(e){for(var s=t.graphs[e.graphId],u=s.nodeMap,c=new $(e),f=n.length-1;f>=0;f--){var h=t.findBackwardEdge(s,c.edges[n[f]],c.edges[n[0]],c);if(h){var l="".concat(t.dfsCode.dfsEdgeList[n[f]].fromNode,"-").concat(h.label);d[l]||(d[l]={projected:[],toNodeId:t.dfsCode.dfsEdgeList[n[f]].fromNode,edgeLabel:h.label}),d[l].projected.push({graphId:e.graphId,edge:h,preNode:e})}}if(!(r>=t.maxNodeNum)){t.findForwardPureEdges(s,c.edges[n[0]],i,c).forEach((function(t){var r="".concat(o,"-").concat(t.label,"-").concat(u[t.to].label);a[r]||(a[r]={projected:[],fromNodeId:o,edgeLabel:t.label,nodeLabel2:u[t.to].label}),a[r].projected.push({graphId:e.graphId,edge:t,preNode:e})}));var v=function(r){t.findForwardRmpathEdges(s,c.edges[n[r]],i,c).forEach((function(o){var i="".concat(t.dfsCode.dfsEdgeList[n[r]].fromNode,"-").concat(o.label,"-").concat(u[o.to].label);a[i]||(a[i]={projected:[],fromNodeId:t.dfsCode.dfsEdgeList[n[r]].fromNode,edgeLabel:o.label,nodeLabel2:u[o.to].label}),a[i].projected.push({graphId:e.graphId,edge:o,preNode:e})}))};for(f=0;f<n.length;f++)v(f)}})),Object.keys(d).forEach((function(e){var r=d[e],n=r.toNodeId,i=r.edgeLabel;t.dfsCode.dfsEdgeList.push(new Y(o,n,"-1",i,"-1")),t.subGraphMining(d[e].projected),t.dfsCode.dfsEdgeList.pop()})),Object.keys(a).forEach((function(e){var r=a[e],n=r.fromNodeId,i=r.edgeLabel,d=r.nodeLabel2;t.dfsCode.dfsEdgeList.push(new Y(n,o+1,V,i,d)),t.subGraphMining(a[e].projected),t.dfsCode.dfsEdgeList.pop()}))}},e.prototype.generate1EdgeFrequentSubGraphs=function(){var e=this.graphs,t=this.directed,r=this.minSupport,n=this.frequentSize1Subgraphs,o={},i={},a={},d={};return Object.keys(e).forEach((function(r){var n=e[r],s=n.nodeMap;n.nodes.forEach((function(e,n){var u=e.label,c="".concat(r,"-").concat(u);if(!a[c]){var f=o[u]||0;f++,o[u]=f}a[c]={graphKey:r,label:u},e.edges.forEach((function(e){var n=u,o=s[e.to].label;if(!t&&n>o){var a=o;o=n,n=a}var c=e.label,f="".concat(r,"-").concat(n,"-").concat(c,"-").concat(o),h="".concat(n,"-").concat(c,"-").concat(o);if(!i[h]){var l=i[h]||0;l++,i[h]=l}d[f]={graphId:r,nodeLabel1:n,edgeLabel:c,nodeLabel2:o}}))}))})),Object.keys(o).forEach((function(e){if(!(o[e]<r)){var t={nodes:[],edges:[]};t.nodes.push({id:"0",label:e}),n.push(t)}})),n},e.prototype.run=function(){var e=this;if(this.frequentSize1Subgraphs=this.generate1EdgeFrequentSubGraphs(),!(this.maxNodeNum<2)){var t=this.graphs,r=(this.directed,{});Object.keys(t).forEach((function(n){var o=t[n],i=o.nodeMap;o.nodes.forEach((function(t){e.findForwardRootEdges(o,t).forEach((function(e){var o=i[e.to],a="".concat(t.label,"-").concat(e.label,"-").concat(o.label);r[a]||(r[a]={projected:[],nodeLabel1:t.label,edgeLabel:e.label,nodeLabel2:o.label});var d={graphId:n,edge:e,preNode:null};r[a].projected.push(d)}))}))})),Object.keys(r).forEach((function(t){var n=r[t],o=n.projected,i=n.nodeLabel1,a=n.edgeLabel,d=n.nodeLabel2;e.dfsCode.dfsEdgeList.push(new Y(0,1,i,a,d)),e.subGraphMining(o),e.dfsCode.dfsEdgeList.pop()}))}},e}(),te="cluster";var re=function(e,t,r,n){void 0===r&&(r="cluster"),void 0===n&&(n=2);var o=[],i=e.nodes;return t.forEach((function(e,t){o.push(ne(i,e,t,r,n))})),o},ne=function(e,t,r,n,o){var i=[r],a=[],d={};return t.forEach((function(t,s){if(t<=o&&r!==s){i.push(s),a.push(e[s]);var u=e[s][n];d[u]?(d[u].count++,d[u].dists.push(t)):d[u]={count:1,dists:[t]}}})),Object.keys(d).forEach((function(e){d[e].dists=d[e].dists.sort((function(e,t){return e-t}))})),{nodeIdx:r,nodeId:e[r].id,nodeIdxs:i,neighbors:a,neighborNum:i.length-1,nodeLabelCountMap:d}},oe=function(e,t,r,n){var o=r.nodes;return n||(n={}),Object.keys(e).forEach((function(i){var a,d;if(!n||!n[i]){n[i]={nodes:[],edges:[]};var s=e[i],u=null===(a=t[s.start])||void 0===a?void 0:a.nodeIdxs,c=null===(d=t[s.end])||void 0===d?void 0:d.nodeIdxs;if(u&&c){var f=new Set(c),h=u.filter((function(e){return f.has(e)}));if(h&&h.length){for(var l={},v=h.length,p=0;p<v;p++){var g=o[h[p]];n[i].nodes.push(g),l[g.id]=!0}r.edges.forEach((function(e){l[e.source]&&l[e.target]&&n[i].edges.push(e)}))}}}})),n},ie=function(e,t,r,n){var o,i,a={};e.nodes.forEach((function(e){a[e.id]=e}));var d=0;return!(null===(o=null==t?void 0:t.edges)||void 0===o?void 0:o.length)||(null===(i=null==t?void 0:t.nodes)||void 0===i?void 0:i.length)<2?0:(e.edges.forEach((function(e){var o=a[e.source][r],i=a[e.target][r],s=null==t?void 0:t.nodes[0][r],u=null==t?void 0:t.nodes[1][r],c=null==t?void 0:t.edges[0][n];e[n]===c&&(o===s&&i===u||o===u&&i===s)&&d++})),d)},ae=function(e,t){var r={},n={};return e.forEach((function(e,o){r[e.id]={idx:o,node:e,degree:0,inDegree:0,outDegree:0};var i=e[t];n[i]||(n[i]=[]),n[i].push(e)})),{nodeMap:r,nodeLabelMap:n}},de=function(e,t,r){var n={},o={};return e.forEach((function(e,i){n["".concat(u)]={idx:i,edge:e};var a=e[t];o[a]||(o[a]=[]),o[a].push(e);var d=r[e.source];d&&(d.degree++,d.outDegree++);var s=r[e.target];s&&(s.degree++,s.inDegree++)})),{edgeMap:n,edgeLabelMap:o}},se=function(e,t,r){var n=t.length,o={};return t.forEach((function(t,i){for(var a=r?0:i+1,d=e[i].id,s=a;s<n;s++)if(i!==s){var u=e[s].id,c=t[s];o["".concat(d,"-").concat(u)]=c,r||(o["".concat(u,"-").concat(d)]=c)}})),o},ue=function(e,t,r,n,o,i,a,d,s,u,c){var f,h="".concat(t.id,"-").concat(r.id);if(u&&u[h])return u[h];var l=c?c[h]:void 0;if(!l){var v=((f={})[h]={start:n[t.id].idx,end:n[r.id].idx,distance:o},f);l=(c=oe(v,i,e,c))[h]}return ie(l,a,d,s)},ce=function(e,t,r,n){var o,i,a,d=null===(o=e[t])||void 0===o?void 0:o.degree,s=null===(i=e[t])||void 0===i?void 0:i.inDegree,u=null===(a=e[t])||void 0===a?void 0:a.outDegree;return void 0===e[t]&&(d=1/0,s=1/0,u=1/0,n[t].forEach((function(e){var t=r[e.id].degree;d>t&&(d=t);var n=r[e.id].inDegree;s>n&&(s=n);var o=r[e.id].outDegree;u>o&&(u=o)})),e[t]={degree:d,inDegree:s,outDegree:u}),{minPatternNodeLabelDegree:d,minPatternNodeLabelInDegree:s,minPatternNodeLabelOutDegree:u}};const fe=function(e,t,r,n,o,i,a){var d;if(void 0===r&&(r=!1),void 0===i&&(i="cluster"),void 0===a&&(a="cluster"),e&&e.nodes){var s=e.nodes.length;if(s){var u=P(e,r),c=P(t,r),f=se(e.nodes,u,r),h=se(t.nodes,c,r),l=ae(e.nodes,i),v=l.nodeMap,p=l.nodeLabelMap,g=ae(t.nodes,i),b=g.nodeMap,E=g.nodeLabelMap;de(e.edges,a,v);var m=de(t.edges,a,b).edgeLabelMap,L=[];null==c||c.forEach((function(e){L=L.concat(e)})),o||(o=Math.max.apply(Math,y(y([],L,!1),[2],!1))),n||(n=o);var N=re(e,u,i,n),w=re(t,c,i,n),M=function(e,t,r,n,o){var i=Math.ceil(r/t),a={},d=0;return n.forEach((function(e,n){for(var s=0,u=0,c=e.nodeIdxs,f=e.neighborNum-1;s<i;){for(var h=c[1+Math.floor(Math.random()*f)],l=0;(a["".concat(n,"-").concat(h)]||a["".concat(h,"-").concat(n)])&&(h=Math.floor(Math.random()*t),!(++l>2*t)););if(l<2*t&&(a["".concat(n,"-").concat(h)]={start:n,end:h,distance:o[n][h]},s++,++d>=r))return a;if(++u>2*t)break}s<i&&(i=(i+(i-s))/(t-n-1))})),a}(0,s,Math.min(100,s*(s-1)/2),N,u),I=oe(M,N,e),j=function(e){var t=e.graphs,r=e.directed,n=void 0!==r&&r,o=e.nodeLabelProp,i=void 0===o?te:o,a=e.edgeLabelProp,d=void 0===a?te:a,s=function(e,t,r,n){var o={};return Object.keys(e).forEach((function(i,a){var d=e[i],s=new X(a,!0,t),u={};d.nodes.forEach((function(e,t){s.addNode(t,e[r]),u[e.id]=t})),d.edges.forEach((function(e,t){var r=u[e.source],o=u[e.target];s.addEdge(-1,r,o,e[n])})),s&&s.getNodeNum()&&(o[s.id]=s)})),o}(t,n,i,d),u=e.minSupport,c=e.maxNodeNum,f=e.minNodeNum,h=e.verbose,l=e.top,v=new ee({graphs:s,minSupport:u,maxNodeNum:c,minNodeNum:f,top:l,verbose:h,directed:n});v.run();var p=function(e,t,r){var n=[];return e.forEach((function(e){var o={nodes:[],edges:[]};e.nodes.forEach((function(e){var r;o.nodes.push(((r={id:"".concat(e.id)})[t]=e.label,r))})),e.edges.forEach((function(e){var t;o.edges.push(((t={source:"".concat(e.from),target:"".concat(e.to)})[r]=e.label,t))})),n.push(o)})),n}(v.frequentSubgraphs,i,d);return p}({graphs:I,nodeLabelProp:i,edgeLabelProp:a,minSupport:1,minNodeNum:1,maxNodeNum:4,directed:r}).slice(0,10),k=j.length,O=[];j.forEach((function(e,t){O[t]={},Object.keys(I).forEach((function(r){var n=I[r],o=ie(n,e,i,a);O[t][r]=o}))}));var D=function(e,t,r){for(var n=1/0,o=0,i=function(t){var r=e[t],i=Object.keys(r).sort((function(e,t){return r[e]-r[t]})),a=[];i.forEach((function(e,t){a[t%10]||(a[t%10]={graphs:[],totalCount:0,aveCount:0}),a[t%10].graphs.push(e),a[t%10].totalCount+=r[e]}));var d=0,s=[];a.forEach((function(e){var t=e.totalCount/e.graphs.length;e.aveCount=t,s.push(t);var n=0,o=e.length;e.graphs.forEach((function(t,o){var i=r[t];e.graphs.forEach((function(e,t){o!==t&&(n+=Math.abs(i-r[e]))}))})),d+=n/=o*(o-1)/2})),d/=a.length;var u=0;s.forEach((function(e,t){s.forEach((function(r,n){t!==n&&(u+=Math.abs(e-r))})),u/=s.length*(s.length-1)/2}));var c=u-d;n<c&&(n=c,o=t)},a=0;a<t;a++)i(a);return{structure:r[o],structureCountMap:e[o]}}(O,k,j),S=D.structure,C=D.structureCountMap,T=t.nodes[0],q=[],A=null===(d=t.nodes[0])||void 0===d?void 0:d[i],F=-1/0;t.nodes.forEach((function(e){var t=e[i],r=p[t];(null==r?void 0:r.length)>F&&(F=r.length,q=r,A=t,T=e)}));var R={},U={},G={},z={},_={},B={};Object.keys(E).forEach((function(n,o){_[n]=[],r&&(B[n]=[]);var d=-1/0,s=E[n],u={};s.forEach((function(e){var t=h["".concat(T.id,"-").concat(e.id)];if(t&&_[n].push(t),d<t&&(d=t),u["".concat(T.id,"-").concat(e.id)]={start:0,end:b[e.id].idx,distance:t},r){var o=h["".concat(e.id,"-").concat(T.id)];o&&B[n].push(o)}})),_[n]=_[n].sort((function(e,t){return e-t})),r&&(B[n]=B[n].sort((function(e,t){return e-t}))),U=oe(u,w,t,U);var c=[];if(Object.keys(u).forEach((function(e){if(G[e])c.push(G[e]);else{var t=U[e];G[e]=ie(t,S,i,a),c.push(G[e])}})),c=c.sort((function(e,t){return t-e})),z["".concat(T.id,"-").concat(n)]=c,n!==A)for(var l=function(t){var r=q[t],o=N[v[r.id].idx],d=o.nodeLabelCountMap[n],s=E[n].length;if(!d||d.count<s)return q.splice(t,1),"continue";for(var u=!1,h=0;h<s;h++)if(d.dists[h]>_[n][h]){u=!0;break}if(u)return q.splice(t,1),"continue";var l={};o.neighbors.forEach((function(e){var t=f["".concat(r.id,"-").concat(e.id)];l["".concat(r.id,"-").concat(e.id)]={start:v[r.id].idx,end:v[e.id].idx,distance:t}})),I=oe(l,N,e,I);var p=[];Object.keys(l).forEach((function(e){if(C[e])p.push(C[e]);else{var t=I[e];C[e]=ie(t,S,i,a),p.push(C[e])}})),p=p.sort((function(e,t){return t-e}));var g=!1;for(h=0;h<s;h++)if(p[h]<c[h]){g=!0;break}return g?(q.splice(t,1),"continue"):void 0},p=((null==q?void 0:q.length)||0)-1;p>=0;p--)l(p)}));var H=[];null==q||q.forEach((function(n){for(var d=v[n.id].idx,s=ne(e.nodes,u[d],d,i,o).neighbors,c=!1,h=s.length-1;h>=0;h--){if(s.length+1<t.nodes.length)return void(c=!0);var l=s[h],p=l[i];if(E[p]&&E[p].length)if(_[p]&&_[p].length){var g="".concat(n.id,"-").concat(l.id),y=f[g],m=_[p].length-1;if(y>_[p][m])s.splice(h,1);else{if(r){var L="".concat(l.id,"-").concat(n.id),w=f[L];if(m=B[p].length-1,w>B[p][m]){s.splice(h,1);continue}}var M=C[g]?C[g]:ue(e,n,l,v,y,N,S,i,a,C,I),j="".concat(T.id,"-").concat(p);if(M<z[j][z[j].length-1])s.splice(h,1);else{var k=ce(R,p,b,E),x=k.minPatternNodeLabelDegree;k.minPatternNodeLabelInDegree,k.minPatternNodeLabelOutDegree,v[l.id].degree<x&&s.splice(h,1)}}}else s.splice(h,1);else s.splice(h,1)}c||H.push({nodes:[n].concat(s)})}));var W=x(t,T.id,!1).length,K={};r?(Object.keys(W).forEach((function(e){var t=b[e].node[i];K[t]?K[t].push(W[e]):K[t]=[W[e]]})),Object.keys(K).forEach((function(e){K[e].sort((function(e,t){return e-t}))}))):K=_;for(var V=function(n){var o=H[n],d=o.nodes[0],s={},u={};o.nodes.forEach((function(e,t){u[e.id]={idx:t,node:e,degree:0,inDegree:0,outDegree:0};var r=e[i];s[r]?s[r]++:s[r]=1}));var c=[],f={};e.edges.forEach((function(e){u[e.source]&&u[e.target]&&(c.push(e),f[e[a]]?f[e[a]]++:f[e[a]]=1,u[e.source].degree++,u[e.target].degree++,u[e.source].outDegree++,u[e.target].inDegree++)}));for(var h=Object.keys(m).length,l=!1,p=0;p<h;p++){var g=Object.keys(m)[p];if(!f[g]||f[g]<m[g].length){l=!0;break}}if(l)return H.splice(n,1),"continue";var y=c.length;if(y<t.edges.length)return H.splice(n,1),"break";var L=!1,N=function(e){var t=c[e],n=t[a],o=m[n];if(!o||!o.length)return f[n]--,o&&f[n]<o.length?(L=!0,"break"):(c.splice(e,1),u[t.source].degree--,u[t.target].degree--,u[t.source].outDegree--,u[t.target].inDegree--,"continue");var d=u[t.source].node[i],s=u[t.target].node[i],h=!1;return o.forEach((function(e){var t=b[e.source].node,n=b[e.target].node;t[i]===d&&n[i]===s&&(h=!0),r||t[i]!==s||n[i]!==d||(h=!0)})),h?void 0:(f[n]--,o&&f[n]<o.length?(L=!0,"break"):(c.splice(e,1),u[t.source].degree--,u[t.target].degree--,u[t.source].outDegree--,u[t.target].inDegree--,"continue"))};for(p=y-1;p>=0&&"break"!==N(p);p--);if(L)return H.splice(n,1),"continue";o.edges=c;var w=x(o,o.nodes[0].id,!1).length;if(Object.keys(w).reverse().forEach((function(e){if(e!==o.nodes[0].id&&!L){if(w[e]===1/0){var t=u[e].node[i];if(s[t]--,s[t]<E[t].length)return void(L=!0);var r=o.nodes.indexOf(u[e].node);return o.nodes.splice(r,1),void(u[e]=void 0)}var n=v[e].node[i];if(!K[n]||!K[n].length||w[e]>K[n][K[n].length-1]){if(t=u[e].node[i],s[t]--,s[t]<E[t].length)return void(L=!0);r=o.nodes.indexOf(u[e].node),o.nodes.splice(r,1),u[e]=void 0}}})),L)return H.splice(n,1),"continue";for(var M=!0,I=0;M&&!L;){if(M=!1,r?u[d.id].degree<b[T.id].degree||u[d.id].inDegree<b[T.id].inDegree||u[d.id].outDegree<b[T.id].outDegree:u[d.id].degree<b[T.id].degree){L=!0;break}if(s[d[i]]<E[d[i]].length){L=!0;break}for(var j=o.nodes.length-1;j>=0;j--){var k=o.nodes[j],O=u[k.id].degree,D=u[k.id].inDegree,S=u[k.id].outDegree,P=k[i],C=ce(R,P,b,E),q=C.minPatternNodeLabelDegree,A=C.minPatternNodeLabelInDegree,F=C.minPatternNodeLabelOutDegree;if(r?O<q||D<A||S<F:O<q){if(s[k[i]]--,s[k[i]]<E[k[i]].length){L=!0;break}o.nodes.splice(j,1),u[k.id]=void 0,M=!0}}if(L||!M&&0!==I)break;for(var U=(y=c.length)-1;U>=0;U--){var G=c[U];if(!u[G.source]||!u[G.target]){c.splice(U,1);var z=G[a];if(f[z]--,u[G.source]&&(u[G.source].degree--,u[G.source].outDegree--),u[G.target]&&(u[G.target].degree--,u[G.target].inDegree--),m[z]&&f[z]<m[z].length){L=!0;break}M=!0}}I++}return L||L||o.nodes.length<t.nodes.length||c.length<t.edges.length?(H.splice(n,1),"continue"):void 0},J=H.length-1;J>=0&&"break"!==V(J);J--);var Q=H.length,Y=function(e){var t=H[e],r={};t.edges.forEach((function(e){var t="".concat(e.source,"-").concat(e.target,"-").concat(e.label);r[t]?r[t]++:r[t]=1}));for(var n=function(e){var t=H[e],n={};t.edges.forEach((function(e){var t="".concat(e.source,"-").concat(e.target,"-").concat(e.label);n[t]?n[t]++:n[t]=1}));var o=!0;Object.keys(n).length!==Object.keys(r).length?o=!1:Object.keys(r).forEach((function(e){n[e]!==r[e]&&(o=!1)})),o&&H.splice(e,1)},o=Q-1;o>e;o--)n(o);Q=H.length};for(J=0;J<=Q-1;J++)Y(J);return H}}};var he="undefined"!=typeof self?self:{};he.onmessage=function(e){var r=e.data,n=r._algorithmType,o=r.data;if(n)if("function"!=typeof t[n])he.postMessage({_algorithmType:"FAILURE"});else{var i=t[n].apply(t,o);he.postMessage({_algorithmType:"SUCCESS",data:i})}}})();
 //# sourceMappingURL=index.worker.js.map

es/i-louvain.d.ts

@@ -8,5 +8,8 @@ import type { ClusterData, GraphData } from './types';
  * @param threshold 差值阈值
+ * @param propertyKey 属性的字段名
+ * @param involvedKeys 参与计算的key集合
+ * @param uninvolvedKeys 不参与计算的key集合
  * @param inertialWeight 惯性模块度权重
  */
-declare const iLouvain: (graphData: GraphData, directed?: boolean, weightPropertyName?: string, threshold?: number, inertialWeight?: number) => ClusterData;
+declare const iLouvain: (graphData: GraphData, directed?: boolean, weightPropertyName?: string, threshold?: number, propertyKey?: string, involvedKeys?: string[], uninvolvedKeys?: string[], inertialWeight?: number) => ClusterData;
 export default iLouvain;

es/i-louvain.js

@@ -8,6 +8,9 @@ import louvain from './louvain';
  * @param threshold 差值阈值
+ * @param propertyKey 属性的字段名
+ * @param involvedKeys 参与计算的key集合
+ * @param uninvolvedKeys 不参与计算的key集合
  * @param inertialWeight 惯性模块度权重
  */
 
-var iLouvain = function iLouvain(graphData, directed, weightPropertyName, threshold, inertialWeight) {
+var iLouvain = function iLouvain(graphData, directed, weightPropertyName, threshold, propertyKey, involvedKeys, uninvolvedKeys, inertialWeight) {
   if (directed === void 0) {
@@ -25,2 +28,14 @@ directed = false;
 
+  if (propertyKey === void 0) {
+    propertyKey = undefined;
+  }
+
+  if (involvedKeys === void 0) {
+    involvedKeys = [];
+  }
+
+  if (uninvolvedKeys === void 0) {
+    uninvolvedKeys = ['id'];
+  }
+
   if (inertialWeight === void 0) {
@@ -30,5 +45,5 @@ inertialWeight = 1;
 
-  return louvain(graphData, directed, weightPropertyName, threshold, true, inertialWeight);
+  return louvain(graphData, directed, weightPropertyName, threshold, true, propertyKey, involvedKeys, uninvolvedKeys, inertialWeight);
 };
 
 export default iLouvain;

es/index.d.ts

@@ -58,8 +58,8 @@ import getAdjMatrix from './adjacent-matrix';
     labelPropagation: (graphData: import("./types").GraphData, directed?: boolean, weightPropertyName?: string, maxIteration?: number) => import("./types").ClusterData;
-    louvain: (graphData: import("./types").GraphData, directed?: boolean, weightPropertyName?: string, threshold?: number, inertialModularity?: boolean, inertialWeight?: number) => import("./types").ClusterData;
-    iLouvain: (graphData: import("./types").GraphData, directed?: boolean, weightPropertyName?: string, threshold?: number, inertialWeight?: number) => import("./types").ClusterData;
+    louvain: (graphData: import("./types").GraphData, directed?: boolean, weightPropertyName?: string, threshold?: number, inertialModularity?: boolean, propertyKey?: string, involvedKeys?: string[], uninvolvedKeys?: string[], inertialWeight?: number) => import("./types").ClusterData;
+    iLouvain: (graphData: import("./types").GraphData, directed?: boolean, weightPropertyName?: string, threshold?: number, propertyKey?: string, involvedKeys?: string[], uninvolvedKeys?: string[], inertialWeight?: number) => import("./types").ClusterData;
     kCore: (graphData: import("./types").GraphData, k?: number) => import("./types").GraphData;
-    kMeans: (data: import("./types").GraphData, k?: number, involvedKeys?: string[], uninvolvedKeys?: string[], propertyKey?: string, distanceType?: import("./types").DistanceType) => import("./types").ClusterData;
+    kMeans: (data: import("./types").GraphData, k?: number, propertyKey?: string, involvedKeys?: string[], uninvolvedKeys?: string[], distanceType?: import("./types").DistanceType) => import("./types").ClusterData;
     cosineSimilarity: (item: number[], targetItem: number[]) => number;
-    nodesCosineSimilarity: (nodes: import("./types").NodeConfig[], seedNode: import("./types").NodeConfig, involvedKeys?: string[], uninvolvedKeys?: string[], propertyKey?: string) => {
+    nodesCosineSimilarity: (nodes: import("./types").NodeConfig[], seedNode: import("./types").NodeConfig, propertyKey?: string, involvedKeys?: string[], uninvolvedKeys?: string[]) => {
         allCosineSimilarity: number[];
@@ -66,0 +66,0 @@ similarNodes: import("./types").NodeConfig[];

es/k-means.d.ts

 import { GraphData, ClusterData, DistanceType } from './types';
 /**
- *  k-means算法 根据节点属性之间的欧氏距离将节点聚类为K个簇
+ *  k-means算法 根据节点之间的距离将节点聚类为K个簇
  * @param data 图数据
  * @param k 质心（聚类中心）个数
- * @param seedNode 种子节点
+ * @param propertyKey 属性的字段名
  * @param involvedKeys 参与计算的key集合
  * @param uninvolvedKeys 不参与计算的key集合
- * @param propertyKey 属性的字段名
- * @param distanceType 距离类型
+ * @param distanceType 距离类型 默认节点属性的欧式距离
  */
-declare const kMeans: (data: GraphData, k?: number, involvedKeys?: string[], uninvolvedKeys?: string[], propertyKey?: string, distanceType?: DistanceType) => ClusterData;
+declare const kMeans: (data: GraphData, k?: number, propertyKey?: string, involvedKeys?: string[], uninvolvedKeys?: string[], distanceType?: DistanceType) => ClusterData;
 export default kMeans;

es/k-means.js

@@ -0,17 +1,34 @@
+import { isEqual } from '@antv/util';
 import { getAllProperties } from './utils/node-properties';
 import { oneHot, getDistance } from './utils/data-preprocessing';
 import Vector from './utils/vector';
-import { DistanceType } from './types';
+import { DistanceType } from './types'; // 获取质心
+
+var getCentroid = function getCentroid(distanceType, allPropertiesWeight, index) {
+  var centroid = [];
+
+  switch (distanceType) {
+    case DistanceType.EuclideanDistance:
+      centroid = allPropertiesWeight[index];
+      break;
+
+    default:
+      centroid = [];
+      break;
+  }
+
+  return centroid;
+};
 /**
- *  k-means算法 根据节点属性之间的欧氏距离将节点聚类为K个簇
+ *  k-means算法 根据节点之间的距离将节点聚类为K个簇
  * @param data 图数据
  * @param k 质心（聚类中心）个数
- * @param seedNode 种子节点
+ * @param propertyKey 属性的字段名
  * @param involvedKeys 参与计算的key集合
  * @param uninvolvedKeys 不参与计算的key集合
- * @param propertyKey 属性的字段名
- * @param distanceType 距离类型
+ * @param distanceType 距离类型 默认节点属性的欧式距离
  */
 
-var kMeans = function kMeans(data, k, involvedKeys, uninvolvedKeys, propertyKey, distanceType) {
+
+var kMeans = function kMeans(data, k, propertyKey, involvedKeys, uninvolvedKeys, distanceType) {
   if (k === void 0) {
@@ -21,2 +38,6 @@ k = 3;
 
+  if (propertyKey === void 0) {
+    propertyKey = undefined;
+  }
+
   if (involvedKeys === void 0) {
@@ -27,9 +48,5 @@ involvedKeys = [];
   if (uninvolvedKeys === void 0) {
-    uninvolvedKeys = [];
+    uninvolvedKeys = ['id'];
   }
 
-  if (propertyKey === void 0) {
-    propertyKey = 'properties';
-  }
-
   if (distanceType === void 0) {
@@ -40,8 +57,27 @@ distanceType = DistanceType.EuclideanDistance;
   var nodes = data.nodes,
-      edges = data.edges; // 所有节点属性集合
+      edges = data.edges; // 距离类型为欧式距离且没有属性时，直接return
 
-  var properties = getAllProperties(nodes, propertyKey); // 所有节点属性one-hot特征向量集合s
+  if (distanceType === DistanceType.EuclideanDistance && !nodes.every(function (node) {
+    return node.hasOwnProperty(propertyKey);
+  })) {
+    return {
+      clusters: [{
+        id: "0",
+        nodes: nodes
+      }],
+      clusterEdges: []
+    };
+  } // 所有节点属性集合
 
-  var allPropertiesWeight = oneHot(properties, involvedKeys, uninvolvedKeys); // 记录节点的原始index，与allPropertiesWeight对应
 
+  var properties = []; // 所有节点属性one-hot特征向量集合
+
+  var allPropertiesWeight = [];
+
+  if (distanceType === DistanceType.EuclideanDistance) {
+    properties = getAllProperties(nodes, propertyKey);
+    allPropertiesWeight = oneHot(properties, involvedKeys, uninvolvedKeys);
+  } // 记录节点的原始index，与allPropertiesWeight对应
+
+
   for (var i = 0; i < nodes.length; i++) {
@@ -60,3 +96,13 @@ nodes[i].originIndex = i;
       var randomIndex = Math.floor(Math.random() * nodes.length);
-      centroids[i] = allPropertiesWeight[randomIndex];
+
+      switch (distanceType) {
+        case DistanceType.EuclideanDistance:
+          centroids[i] = allPropertiesWeight[randomIndex];
+          break;
+
+        default:
+          centroids[i] = [];
+          break;
+      }
+
       centroidIndexList.push(randomIndex);
@@ -67,5 +113,5 @@ clusters[i] = [nodes[randomIndex]];
       var maxDistance = -Infinity;
-      var maxDistanceIndex = 0; // 选取与已有质心平均距离最远的点做为新的质心
+      var maxDistanceNodeIndex = 0;
 
-      for (var m = 0; m < nodes.length; m++) {
+      var _loop_1 = function _loop_1(m) {
         if (!centroidIndexList.includes(m)) {
@@ -75,4 +121,14 @@ var totalDistance = 0;
           for (var j = 0; j < centroids.length; j++) {
-            // 求节点到质心距离（默认欧式距离）
-            var distance = getDistance(allPropertiesWeight[nodes[m].originIndex], centroids[j], distanceType);
+            // 求节点到质心的距离（默认节点属性的欧式距离）
+            var distance = 0;
+
+            switch (distanceType) {
+              case DistanceType.EuclideanDistance:
+                distance = getDistance(allPropertiesWeight[nodes[m].originIndex], centroids[j], distanceType);
+                break;
+
+              default:
+                break;
+            }
+
             totalDistance += distance;
@@ -84,13 +140,20 @@ } // 节点到各质心的平均距离（默认欧式距离）
 
-          if (avgDistance > maxDistance) {
+          if (avgDistance > maxDistance && !centroids.find(function (centroid) {
+            return isEqual(centroid, getCentroid(distanceType, allPropertiesWeight, nodes[m].originIndex));
+          })) {
             maxDistance = avgDistance;
-            maxDistanceIndex = m;
+            maxDistanceNodeIndex = m;
           }
         }
+      }; // 选取与已有质心平均距离最远的点做为新的质心
+
+
+      for (var m = 0; m < nodes.length; m++) {
+        _loop_1(m);
       }
 
-      centroids[i] = allPropertiesWeight[maxDistanceIndex];
-      centroidIndexList.push(maxDistanceIndex);
-      clusters[i] = [nodes[maxDistanceIndex]];
-      nodes[maxDistanceIndex].clusterId = String(i);
+      centroids[i] = getCentroid(distanceType, allPropertiesWeight, maxDistanceNodeIndex);
+      centroidIndexList.push(maxDistanceNodeIndex);
+      clusters[i] = [nodes[maxDistanceNodeIndex]];
+      nodes[maxDistanceNodeIndex].clusterId = String(i);
     }
@@ -108,5 +171,15 @@ }
         for (var j = 0; j < centroids.length; j++) {
-          // 求节点到质心的距离（默认欧式距离）
-          var distance = getDistance(allPropertiesWeight[i], centroids[j], distanceType); // 记录节点最近的质心的索引
+          // 求节点到质心的距离（默认节点属性的欧式距离）
+          var distance = 0;
 
+          switch (distanceType) {
+            case DistanceType.EuclideanDistance:
+              distance = getDistance(allPropertiesWeight[i], centroids[j], distanceType);
+              break;
+
+            default:
+              break;
+          } // 记录节点最近的质心的索引
+
+
           if (distance < minDistance) {
@@ -119,13 +192,13 @@ minDistance = distance;
 
-        if (nodes[i].clusterId !== String(minDistanceIndex)) {
-          for (var n = 0; n < clusters[minDistanceIndex].length; n++) {
-            if (clusters[minDistanceIndex][n].id === nodes[i].id) {
-              clusters[minDistanceIndex].splice(n, 1);
+        if (nodes[i].clusterId !== undefined) {
+          for (var n = clusters[Number(nodes[i].clusterId)].length - 1; n >= 0; n--) {
+            if (clusters[Number(nodes[i].clusterId)][n].id === nodes[i].id) {
+              clusters[Number(nodes[i].clusterId)].splice(n, 1);
             }
-          } // 将节点划分到距离最小的质心（聚类中心）所对应的类中
+          }
+        } // 将节点划分到距离最小的质心（聚类中心）所对应的类中
 
 
-          clusters[minDistanceIndex].push(nodes[i]);
-          nodes[i].clusterId = String(minDistanceIndex);
-        }
+        nodes[i].clusterId = String(minDistanceIndex);
+        clusters[minDistanceIndex].push(nodes[i]);
       }
@@ -155,5 +228,7 @@ } // 是否存在质心（聚类中心）移动
 
-    iterations++; // 如果不存在质心（聚类中心）移动或者迭代次数超过1000，则停止
+    iterations++; // 如果每个节点都归属了类别，且不存在质心（聚类中心）移动或者迭代次数超过1000，则停止
 
-    if (centroidsEqualAvg || iterations >= 1000) {
+    if (nodes.every(function (node) {
+      return node.clusterId !== undefined;
+    }) && centroidsEqualAvg || iterations >= 1000) {
       break;
@@ -160,0 +235,0 @@ }

es/louvain.d.ts

@@ -9,4 +9,8 @@ import { ClusterData, GraphData } from './types';
  * @param inertialModularity 是否使用惯性模块度（即节点属性相似性）
+ * @param propertyKey 属性的字段名
+ * @param involvedKeys 参与计算的key集合
+ * @param uninvolvedKeys 不参与计算的key集合
+ * @param inertialWeight 惯性模块度权重
  */
-declare const louvain: (graphData: GraphData, directed?: boolean, weightPropertyName?: string, threshold?: number, inertialModularity?: boolean, inertialWeight?: number) => ClusterData;
+declare const louvain: (graphData: GraphData, directed?: boolean, weightPropertyName?: string, threshold?: number, inertialModularity?: boolean, propertyKey?: string, involvedKeys?: string[], uninvolvedKeys?: string[], inertialWeight?: number) => ClusterData;
 export default louvain;

es/louvain.js

@@ -100,6 +100,10 @@ import { clone } from '@antv/util';
  * @param inertialModularity 是否使用惯性模块度（即节点属性相似性）
+ * @param propertyKey 属性的字段名
+ * @param involvedKeys 参与计算的key集合
+ * @param uninvolvedKeys 不参与计算的key集合
+ * @param inertialWeight 惯性模块度权重
  */
 
 
-var louvain = function louvain(graphData, directed, weightPropertyName, threshold, inertialModularity, inertialWeight) {
+var louvain = function louvain(graphData, directed, weightPropertyName, threshold, inertialModularity, propertyKey, involvedKeys, uninvolvedKeys, inertialWeight) {
   if (directed === void 0) {
@@ -121,2 +125,14 @@ directed = false;
 
+  if (propertyKey === void 0) {
+    propertyKey = undefined;
+  }
+
+  if (involvedKeys === void 0) {
+    involvedKeys = [];
+  }
+
+  if (uninvolvedKeys === void 0) {
+    uninvolvedKeys = ['id'];
+  }
+
   if (inertialWeight === void 0) {
@@ -154,5 +170,5 @@ inertialWeight = 1;
 
-    var properties = getAllProperties(nodes); // 所有节点属性one-hot特征向量集合
+    var properties = getAllProperties(nodes, propertyKey); // 所有节点属性one-hot特征向量集合
 
-    allPropertiesWeight = oneHot(properties);
+    allPropertiesWeight = oneHot(properties, involvedKeys, uninvolvedKeys);
   }
@@ -159,0 +175,0 @@

es/nodes-cosine-similarity.d.ts

@@ -6,7 +6,7 @@ import { NodeConfig } from './types';
  * @param seedNode 种子节点
+ * @param propertyKey 属性的字段名
  * @param involvedKeys 参与计算的key集合
  * @param uninvolvedKeys 不参与计算的key集合
- * @param propertyKey 属性的字段名
  */
-declare const nodesCosineSimilarity: (nodes: NodeConfig[], seedNode: NodeConfig, involvedKeys?: string[], uninvolvedKeys?: string[], propertyKey?: string) => {
+declare const nodesCosineSimilarity: (nodes: NodeConfig[], seedNode: NodeConfig, propertyKey?: string, involvedKeys?: string[], uninvolvedKeys?: string[]) => {
     allCosineSimilarity: number[];
@@ -13,0 +13,0 @@ similarNodes: NodeConfig[];

es/nodes-cosine-similarity.js

@@ -9,8 +9,8 @@ import { clone } from '@antv/util';
  * @param seedNode 种子节点
+ * @param propertyKey 属性的字段名
  * @param involvedKeys 参与计算的key集合
  * @param uninvolvedKeys 不参与计算的key集合
- * @param propertyKey 属性的字段名
  */
 
-var nodesCosineSimilarity = function nodesCosineSimilarity(nodes, seedNode, involvedKeys, uninvolvedKeys, propertyKey) {
+var nodesCosineSimilarity = function nodesCosineSimilarity(nodes, seedNode, propertyKey, involvedKeys, uninvolvedKeys) {
   if (nodes === void 0) {
@@ -20,2 +20,6 @@ nodes = [];
 
+  if (propertyKey === void 0) {
+    propertyKey = undefined;
+  }
+
   if (involvedKeys === void 0) {
@@ -29,6 +33,2 @@ involvedKeys = [];
 
-  if (propertyKey === void 0) {
-    propertyKey = 'properties';
-  }
-
   var similarNodes = clone(nodes.filter(function (node) {
@@ -41,3 +41,3 @@ return node.id !== seedNode.id;
 
-  var properties = getAllProperties(nodes, propertyKey); // 所有节点属性one-hot特征向量集合s
+  var properties = getAllProperties(nodes, propertyKey); // 所有节点属性one-hot特征向量集合
 
@@ -44,0 +44,0 @@ var allPropertiesWeight = oneHot(properties, involvedKeys, uninvolvedKeys); // 种子节点属性

es/utils/data-preprocessing.js

@@ -50,24 +50,43 @@ import { uniq } from '@antv/util';
   var allKeyValueMap = getAllKeyValueMap(dataList, involvedKeys, uninvolvedKeys);
-  var oneHotCode = []; // 对数据进行one-hot编码
+  var oneHotCode = [];
 
+  if (!Object.keys(allKeyValueMap).length) {
+    return;
+  } // 对数据进行one-hot编码
+
+
   dataList.forEach(function (data, index) {
     var code = [];
-    Object.keys(allKeyValueMap).forEach(function (key) {
-      var keyValue = data[key];
-      var allKeyValue = allKeyValueMap[key];
-      var valueIndex = allKeyValue.findIndex(function (value) {
-        return keyValue === value;
-      });
-      var subCode = [];
 
-      for (var i = 0; i < allKeyValue.length; i++) {
-        if (i === valueIndex) {
-          subCode.push(1);
-        } else {
-          subCode.push(0);
-        }
+    if (Object.keys(allKeyValueMap).length === 1) {
+      // 如果只有一个属性且所有的属性值都能转成数值型，则直接用属性值
+      var key = Object.keys(allKeyValueMap)[0];
+      var keyValue = allKeyValueMap[key];
+
+      if (keyValue.every(function (value) {
+        return !isNaN(Number(value));
+      })) {
+        code = [data[key]];
       }
+    } else {
+      Object.keys(allKeyValueMap).forEach(function (key) {
+        var keyValue = data[key];
+        var allKeyValue = allKeyValueMap[key];
+        var valueIndex = allKeyValue.findIndex(function (value) {
+          return keyValue === value;
+        });
+        var subCode = [];
 
-      code = code.concat(subCode);
-    });
+        for (var i = 0; i < allKeyValue.length; i++) {
+          if (i === valueIndex) {
+            subCode.push(1);
+          } else {
+            subCode.push(0);
+          }
+        }
+
+        code = code.concat(subCode);
+      });
+    }
+
     oneHotCode[index] = code;
@@ -74,0 +93,0 @@ });

es/utils/node-properties.d.ts

 import { NodeConfig } from '../types';
 export declare const getAllSortProperties: (nodes?: NodeConfig[], n?: number) => string[];
 export declare const getPropertyWeight: (nodes: NodeConfig[]) => any[];
-export declare const getAllProperties: (nodes: any, key?: string) => any[];
+export declare const getAllProperties: (nodes: any, key?: any) => any[];
 declare const _default: {
     getAllSortProperties: (nodes?: NodeConfig[], n?: number) => string[];
     getPropertyWeight: (nodes: NodeConfig[]) => any[];
-    getAllProperties: (nodes: any, key?: string) => any[];
+    getAllProperties: (nodes: any, key?: any) => any[];
 };
 export default _default;

es/utils/node-properties.js

@@ -75,3 +75,3 @@ import { secondReg, dateReg } from '../constants/time'; // 获取所有属性并排序
   if (key === void 0) {
-    key = 'properties';
+    key = undefined;
   }
@@ -81,7 +81,9 @@
   nodes.forEach(function (node) {
-    if (!node.properties) {
-      return;
+    if (key === undefined) {
+      allProperties.push(node);
     }
 
-    allProperties.push(node[key]);
+    if (node[key] !== undefined) {
+      allProperties.push(node[key]);
+    }
   });
@@ -88,0 +90,0 @@ return allProperties;

lib/detect-cycle.js

 "use strict";
 
-function _typeof(obj) { "@babel/helpers - typeof"; if (typeof Symbol === "function" && typeof Symbol.iterator === "symbol") { _typeof = function _typeof(obj) { return typeof obj; }; } else { _typeof = function _typeof(obj) { return obj && typeof Symbol === "function" && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj; }; } return _typeof(obj); }
+function _typeof(obj) { "@babel/helpers - typeof"; return _typeof = "function" == typeof Symbol && "symbol" == typeof Symbol.iterator ? function (obj) { return typeof obj; } : function (obj) { return obj && "function" == typeof Symbol && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj; }, _typeof(obj); }
 
@@ -5,0 +5,0 @@ Object.defineProperty(exports, "__esModule", {

lib/i-louvain.d.ts

@@ -8,5 +8,8 @@ import type { ClusterData, GraphData } from './types';
  * @param threshold 差值阈值
+ * @param propertyKey 属性的字段名
+ * @param involvedKeys 参与计算的key集合
+ * @param uninvolvedKeys 不参与计算的key集合
  * @param inertialWeight 惯性模块度权重
  */
-declare const iLouvain: (graphData: GraphData, directed?: boolean, weightPropertyName?: string, threshold?: number, inertialWeight?: number) => ClusterData;
+declare const iLouvain: (graphData: GraphData, directed?: boolean, weightPropertyName?: string, threshold?: number, propertyKey?: string, involvedKeys?: string[], uninvolvedKeys?: string[], inertialWeight?: number) => ClusterData;
 export default iLouvain;

lib/i-louvain.js

@@ -18,5 +18,8 @@ "use strict";
  * @param threshold 差值阈值
+ * @param propertyKey 属性的字段名
+ * @param involvedKeys 参与计算的key集合
+ * @param uninvolvedKeys 不参与计算的key集合
  * @param inertialWeight 惯性模块度权重
  */
-var iLouvain = function iLouvain(graphData, directed, weightPropertyName, threshold, inertialWeight) {
+var iLouvain = function iLouvain(graphData, directed, weightPropertyName, threshold, propertyKey, involvedKeys, uninvolvedKeys, inertialWeight) {
   if (directed === void 0) {
@@ -34,2 +37,14 @@ directed = false;
 
+  if (propertyKey === void 0) {
+    propertyKey = undefined;
+  }
+
+  if (involvedKeys === void 0) {
+    involvedKeys = [];
+  }
+
+  if (uninvolvedKeys === void 0) {
+    uninvolvedKeys = ['id'];
+  }
+
   if (inertialWeight === void 0) {
@@ -39,3 +54,3 @@ inertialWeight = 1;
 
-  return (0, _louvain.default)(graphData, directed, weightPropertyName, threshold, true, inertialWeight);
+  return (0, _louvain.default)(graphData, directed, weightPropertyName, threshold, true, propertyKey, involvedKeys, uninvolvedKeys, inertialWeight);
 };
@@ -42,0 +57,0 @@

lib/index.d.ts

@@ -58,8 +58,8 @@ import getAdjMatrix from './adjacent-matrix';
     labelPropagation: (graphData: import("./types").GraphData, directed?: boolean, weightPropertyName?: string, maxIteration?: number) => import("./types").ClusterData;
-    louvain: (graphData: import("./types").GraphData, directed?: boolean, weightPropertyName?: string, threshold?: number, inertialModularity?: boolean, inertialWeight?: number) => import("./types").ClusterData;
-    iLouvain: (graphData: import("./types").GraphData, directed?: boolean, weightPropertyName?: string, threshold?: number, inertialWeight?: number) => import("./types").ClusterData;
+    louvain: (graphData: import("./types").GraphData, directed?: boolean, weightPropertyName?: string, threshold?: number, inertialModularity?: boolean, propertyKey?: string, involvedKeys?: string[], uninvolvedKeys?: string[], inertialWeight?: number) => import("./types").ClusterData;
+    iLouvain: (graphData: import("./types").GraphData, directed?: boolean, weightPropertyName?: string, threshold?: number, propertyKey?: string, involvedKeys?: string[], uninvolvedKeys?: string[], inertialWeight?: number) => import("./types").ClusterData;
     kCore: (graphData: import("./types").GraphData, k?: number) => import("./types").GraphData;
-    kMeans: (data: import("./types").GraphData, k?: number, involvedKeys?: string[], uninvolvedKeys?: string[], propertyKey?: string, distanceType?: import("./types").DistanceType) => import("./types").ClusterData;
+    kMeans: (data: import("./types").GraphData, k?: number, propertyKey?: string, involvedKeys?: string[], uninvolvedKeys?: string[], distanceType?: import("./types").DistanceType) => import("./types").ClusterData;
     cosineSimilarity: (item: number[], targetItem: number[]) => number;
-    nodesCosineSimilarity: (nodes: import("./types").NodeConfig[], seedNode: import("./types").NodeConfig, involvedKeys?: string[], uninvolvedKeys?: string[], propertyKey?: string) => {
+    nodesCosineSimilarity: (nodes: import("./types").NodeConfig[], seedNode: import("./types").NodeConfig, propertyKey?: string, involvedKeys?: string[], uninvolvedKeys?: string[]) => {
         allCosineSimilarity: number[];
@@ -66,0 +66,0 @@ similarNodes: import("./types").NodeConfig[];

lib/index.js

 "use strict";
 
-function _typeof(obj) { "@babel/helpers - typeof"; if (typeof Symbol === "function" && typeof Symbol.iterator === "symbol") { _typeof = function _typeof(obj) { return typeof obj; }; } else { _typeof = function _typeof(obj) { return obj && typeof Symbol === "function" && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj; }; } return _typeof(obj); }
+function _typeof(obj) { "@babel/helpers - typeof"; return _typeof = "function" == typeof Symbol && "symbol" == typeof Symbol.iterator ? function (obj) { return typeof obj; } : function (obj) { return obj && "function" == typeof Symbol && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj; }, _typeof(obj); }
 
@@ -5,0 +5,0 @@ Object.defineProperty(exports, "__esModule", {

lib/k-means.d.ts

 import { GraphData, ClusterData, DistanceType } from './types';
 /**
- *  k-means算法 根据节点属性之间的欧氏距离将节点聚类为K个簇
+ *  k-means算法 根据节点之间的距离将节点聚类为K个簇
  * @param data 图数据
  * @param k 质心（聚类中心）个数
- * @param seedNode 种子节点
+ * @param propertyKey 属性的字段名
  * @param involvedKeys 参与计算的key集合
  * @param uninvolvedKeys 不参与计算的key集合
- * @param propertyKey 属性的字段名
- * @param distanceType 距离类型
+ * @param distanceType 距离类型 默认节点属性的欧式距离
  */
-declare const kMeans: (data: GraphData, k?: number, involvedKeys?: string[], uninvolvedKeys?: string[], propertyKey?: string, distanceType?: DistanceType) => ClusterData;
+declare const kMeans: (data: GraphData, k?: number, propertyKey?: string, involvedKeys?: string[], uninvolvedKeys?: string[], distanceType?: DistanceType) => ClusterData;
 export default kMeans;

lib/k-means.js

@@ -8,2 +8,4 @@ "use strict";
 
+var _util = require("@antv/util");
+
 var _nodeProperties = require("./utils/node-properties");
@@ -19,13 +21,30 @@
 
+// 获取质心
+var getCentroid = function getCentroid(distanceType, allPropertiesWeight, index) {
+  var centroid = [];
+
+  switch (distanceType) {
+    case _types.DistanceType.EuclideanDistance:
+      centroid = allPropertiesWeight[index];
+      break;
+
+    default:
+      centroid = [];
+      break;
+  }
+
+  return centroid;
+};
 /**
- *  k-means算法 根据节点属性之间的欧氏距离将节点聚类为K个簇
+ *  k-means算法 根据节点之间的距离将节点聚类为K个簇
  * @param data 图数据
  * @param k 质心（聚类中心）个数
- * @param seedNode 种子节点
+ * @param propertyKey 属性的字段名
  * @param involvedKeys 参与计算的key集合
  * @param uninvolvedKeys 不参与计算的key集合
- * @param propertyKey 属性的字段名
- * @param distanceType 距离类型
+ * @param distanceType 距离类型 默认节点属性的欧式距离
  */
-var kMeans = function kMeans(data, k, involvedKeys, uninvolvedKeys, propertyKey, distanceType) {
+
+
+var kMeans = function kMeans(data, k, propertyKey, involvedKeys, uninvolvedKeys, distanceType) {
   if (k === void 0) {
@@ -35,2 +54,6 @@ k = 3;
 
+  if (propertyKey === void 0) {
+    propertyKey = undefined;
+  }
+
   if (involvedKeys === void 0) {
@@ -41,9 +64,5 @@ involvedKeys = [];
   if (uninvolvedKeys === void 0) {
-    uninvolvedKeys = [];
+    uninvolvedKeys = ['id'];
   }
 
-  if (propertyKey === void 0) {
-    propertyKey = 'properties';
-  }
-
   if (distanceType === void 0) {
@@ -54,8 +73,27 @@ distanceType = _types.DistanceType.EuclideanDistance;
   var nodes = data.nodes,
-      edges = data.edges; // 所有节点属性集合
+      edges = data.edges; // 距离类型为欧式距离且没有属性时，直接return
 
-  var properties = (0, _nodeProperties.getAllProperties)(nodes, propertyKey); // 所有节点属性one-hot特征向量集合s
+  if (distanceType === _types.DistanceType.EuclideanDistance && !nodes.every(function (node) {
+    return node.hasOwnProperty(propertyKey);
+  })) {
+    return {
+      clusters: [{
+        id: "0",
+        nodes: nodes
+      }],
+      clusterEdges: []
+    };
+  } // 所有节点属性集合
 
-  var allPropertiesWeight = (0, _dataPreprocessing.oneHot)(properties, involvedKeys, uninvolvedKeys); // 记录节点的原始index，与allPropertiesWeight对应
 
+  var properties = []; // 所有节点属性one-hot特征向量集合
+
+  var allPropertiesWeight = [];
+
+  if (distanceType === _types.DistanceType.EuclideanDistance) {
+    properties = (0, _nodeProperties.getAllProperties)(nodes, propertyKey);
+    allPropertiesWeight = (0, _dataPreprocessing.oneHot)(properties, involvedKeys, uninvolvedKeys);
+  } // 记录节点的原始index，与allPropertiesWeight对应
+
+
   for (var i = 0; i < nodes.length; i++) {
@@ -74,3 +112,13 @@ nodes[i].originIndex = i;
       var randomIndex = Math.floor(Math.random() * nodes.length);
-      centroids[i] = allPropertiesWeight[randomIndex];
+
+      switch (distanceType) {
+        case _types.DistanceType.EuclideanDistance:
+          centroids[i] = allPropertiesWeight[randomIndex];
+          break;
+
+        default:
+          centroids[i] = [];
+          break;
+      }
+
       centroidIndexList.push(randomIndex);
@@ -81,5 +129,5 @@ clusters[i] = [nodes[randomIndex]];
       var maxDistance = -Infinity;
-      var maxDistanceIndex = 0; // 选取与已有质心平均距离最远的点做为新的质心
+      var maxDistanceNodeIndex = 0;
 
-      for (var m = 0; m < nodes.length; m++) {
+      var _loop_1 = function _loop_1(m) {
         if (!centroidIndexList.includes(m)) {
@@ -89,4 +137,14 @@ var totalDistance = 0;
           for (var j = 0; j < centroids.length; j++) {
-            // 求节点到质心距离（默认欧式距离）
-            var distance = (0, _dataPreprocessing.getDistance)(allPropertiesWeight[nodes[m].originIndex], centroids[j], distanceType);
+            // 求节点到质心的距离（默认节点属性的欧式距离）
+            var distance = 0;
+
+            switch (distanceType) {
+              case _types.DistanceType.EuclideanDistance:
+                distance = (0, _dataPreprocessing.getDistance)(allPropertiesWeight[nodes[m].originIndex], centroids[j], distanceType);
+                break;
+
+              default:
+                break;
+            }
+
             totalDistance += distance;
@@ -98,13 +156,20 @@ } // 节点到各质心的平均距离（默认欧式距离）
 
-          if (avgDistance > maxDistance) {
+          if (avgDistance > maxDistance && !centroids.find(function (centroid) {
+            return (0, _util.isEqual)(centroid, getCentroid(distanceType, allPropertiesWeight, nodes[m].originIndex));
+          })) {
             maxDistance = avgDistance;
-            maxDistanceIndex = m;
+            maxDistanceNodeIndex = m;
           }
         }
+      }; // 选取与已有质心平均距离最远的点做为新的质心
+
+
+      for (var m = 0; m < nodes.length; m++) {
+        _loop_1(m);
       }
 
-      centroids[i] = allPropertiesWeight[maxDistanceIndex];
-      centroidIndexList.push(maxDistanceIndex);
-      clusters[i] = [nodes[maxDistanceIndex]];
-      nodes[maxDistanceIndex].clusterId = String(i);
+      centroids[i] = getCentroid(distanceType, allPropertiesWeight, maxDistanceNodeIndex);
+      centroidIndexList.push(maxDistanceNodeIndex);
+      clusters[i] = [nodes[maxDistanceNodeIndex]];
+      nodes[maxDistanceNodeIndex].clusterId = String(i);
     }
@@ -122,5 +187,15 @@ }
         for (var j = 0; j < centroids.length; j++) {
-          // 求节点到质心的距离（默认欧式距离）
-          var distance = (0, _dataPreprocessing.getDistance)(allPropertiesWeight[i], centroids[j], distanceType); // 记录节点最近的质心的索引
+          // 求节点到质心的距离（默认节点属性的欧式距离）
+          var distance = 0;
 
+          switch (distanceType) {
+            case _types.DistanceType.EuclideanDistance:
+              distance = (0, _dataPreprocessing.getDistance)(allPropertiesWeight[i], centroids[j], distanceType);
+              break;
+
+            default:
+              break;
+          } // 记录节点最近的质心的索引
+
+
           if (distance < minDistance) {
@@ -133,13 +208,13 @@ minDistance = distance;
 
-        if (nodes[i].clusterId !== String(minDistanceIndex)) {
-          for (var n = 0; n < clusters[minDistanceIndex].length; n++) {
-            if (clusters[minDistanceIndex][n].id === nodes[i].id) {
-              clusters[minDistanceIndex].splice(n, 1);
+        if (nodes[i].clusterId !== undefined) {
+          for (var n = clusters[Number(nodes[i].clusterId)].length - 1; n >= 0; n--) {
+            if (clusters[Number(nodes[i].clusterId)][n].id === nodes[i].id) {
+              clusters[Number(nodes[i].clusterId)].splice(n, 1);
             }
-          } // 将节点划分到距离最小的质心（聚类中心）所对应的类中
+          }
+        } // 将节点划分到距离最小的质心（聚类中心）所对应的类中
 
 
-          clusters[minDistanceIndex].push(nodes[i]);
-          nodes[i].clusterId = String(minDistanceIndex);
-        }
+        nodes[i].clusterId = String(minDistanceIndex);
+        clusters[minDistanceIndex].push(nodes[i]);
       }
@@ -169,5 +244,7 @@ } // 是否存在质心（聚类中心）移动
 
-    iterations++; // 如果不存在质心（聚类中心）移动或者迭代次数超过1000，则停止
+    iterations++; // 如果每个节点都归属了类别，且不存在质心（聚类中心）移动或者迭代次数超过1000，则停止
 
-    if (centroidsEqualAvg || iterations >= 1000) {
+    if (nodes.every(function (node) {
+      return node.clusterId !== undefined;
+    }) && centroidsEqualAvg || iterations >= 1000) {
       break;
@@ -174,0 +251,0 @@ }

lib/louvain.d.ts

@@ -9,4 +9,8 @@ import { ClusterData, GraphData } from './types';
  * @param inertialModularity 是否使用惯性模块度（即节点属性相似性）
+ * @param propertyKey 属性的字段名
+ * @param involvedKeys 参与计算的key集合
+ * @param uninvolvedKeys 不参与计算的key集合
+ * @param inertialWeight 惯性模块度权重
  */
-declare const louvain: (graphData: GraphData, directed?: boolean, weightPropertyName?: string, threshold?: number, inertialModularity?: boolean, inertialWeight?: number) => ClusterData;
+declare const louvain: (graphData: GraphData, directed?: boolean, weightPropertyName?: string, threshold?: number, inertialModularity?: boolean, propertyKey?: string, involvedKeys?: string[], uninvolvedKeys?: string[], inertialWeight?: number) => ClusterData;
 export default louvain;

lib/louvain.js

@@ -113,6 +113,10 @@ "use strict";
  * @param inertialModularity 是否使用惯性模块度（即节点属性相似性）
+ * @param propertyKey 属性的字段名
+ * @param involvedKeys 参与计算的key集合
+ * @param uninvolvedKeys 不参与计算的key集合
+ * @param inertialWeight 惯性模块度权重
  */
 
 
-var louvain = function louvain(graphData, directed, weightPropertyName, threshold, inertialModularity, inertialWeight) {
+var louvain = function louvain(graphData, directed, weightPropertyName, threshold, inertialModularity, propertyKey, involvedKeys, uninvolvedKeys, inertialWeight) {
   if (directed === void 0) {
@@ -134,2 +138,14 @@ directed = false;
 
+  if (propertyKey === void 0) {
+    propertyKey = undefined;
+  }
+
+  if (involvedKeys === void 0) {
+    involvedKeys = [];
+  }
+
+  if (uninvolvedKeys === void 0) {
+    uninvolvedKeys = ['id'];
+  }
+
   if (inertialWeight === void 0) {
@@ -167,5 +183,5 @@ inertialWeight = 1;
 
-    var properties = (0, _nodeProperties.getAllProperties)(nodes); // 所有节点属性one-hot特征向量集合
+    var properties = (0, _nodeProperties.getAllProperties)(nodes, propertyKey); // 所有节点属性one-hot特征向量集合
 
-    allPropertiesWeight = (0, _dataPreprocessing.oneHot)(properties);
+    allPropertiesWeight = (0, _dataPreprocessing.oneHot)(properties, involvedKeys, uninvolvedKeys);
   }
@@ -172,0 +188,0 @@

lib/nodes-cosine-similarity.d.ts

@@ -6,7 +6,7 @@ import { NodeConfig } from './types';
  * @param seedNode 种子节点
+ * @param propertyKey 属性的字段名
  * @param involvedKeys 参与计算的key集合
  * @param uninvolvedKeys 不参与计算的key集合
- * @param propertyKey 属性的字段名
  */
-declare const nodesCosineSimilarity: (nodes: NodeConfig[], seedNode: NodeConfig, involvedKeys?: string[], uninvolvedKeys?: string[], propertyKey?: string) => {
+declare const nodesCosineSimilarity: (nodes: NodeConfig[], seedNode: NodeConfig, propertyKey?: string, involvedKeys?: string[], uninvolvedKeys?: string[]) => {
     allCosineSimilarity: number[];
@@ -13,0 +13,0 @@ similarNodes: NodeConfig[];

lib/nodes-cosine-similarity.js

@@ -22,7 +22,7 @@ "use strict";
  * @param seedNode 种子节点
+ * @param propertyKey 属性的字段名
  * @param involvedKeys 参与计算的key集合
  * @param uninvolvedKeys 不参与计算的key集合
- * @param propertyKey 属性的字段名
  */
-var nodesCosineSimilarity = function nodesCosineSimilarity(nodes, seedNode, involvedKeys, uninvolvedKeys, propertyKey) {
+var nodesCosineSimilarity = function nodesCosineSimilarity(nodes, seedNode, propertyKey, involvedKeys, uninvolvedKeys) {
   if (nodes === void 0) {
@@ -32,2 +32,6 @@ nodes = [];
 
+  if (propertyKey === void 0) {
+    propertyKey = undefined;
+  }
+
   if (involvedKeys === void 0) {
@@ -41,6 +45,2 @@ involvedKeys = [];
 
-  if (propertyKey === void 0) {
-    propertyKey = 'properties';
-  }
-
   var similarNodes = (0, _util.clone)(nodes.filter(function (node) {
@@ -53,3 +53,3 @@ return node.id !== seedNode.id;
 
-  var properties = (0, _nodeProperties.getAllProperties)(nodes, propertyKey); // 所有节点属性one-hot特征向量集合s
+  var properties = (0, _nodeProperties.getAllProperties)(nodes, propertyKey); // 所有节点属性one-hot特征向量集合
 
@@ -56,0 +56,0 @@ var allPropertiesWeight = (0, _dataPreprocessing.oneHot)(properties, involvedKeys, uninvolvedKeys); // 种子节点属性

lib/utils/data-preprocessing.js

@@ -64,24 +64,43 @@ "use strict";
   var allKeyValueMap = getAllKeyValueMap(dataList, involvedKeys, uninvolvedKeys);
-  var oneHotCode = []; // 对数据进行one-hot编码
+  var oneHotCode = [];
 
+  if (!Object.keys(allKeyValueMap).length) {
+    return;
+  } // 对数据进行one-hot编码
+
+
   dataList.forEach(function (data, index) {
     var code = [];
-    Object.keys(allKeyValueMap).forEach(function (key) {
-      var keyValue = data[key];
-      var allKeyValue = allKeyValueMap[key];
-      var valueIndex = allKeyValue.findIndex(function (value) {
-        return keyValue === value;
-      });
-      var subCode = [];
 
-      for (var i = 0; i < allKeyValue.length; i++) {
-        if (i === valueIndex) {
-          subCode.push(1);
-        } else {
-          subCode.push(0);
-        }
+    if (Object.keys(allKeyValueMap).length === 1) {
+      // 如果只有一个属性且所有的属性值都能转成数值型，则直接用属性值
+      var key = Object.keys(allKeyValueMap)[0];
+      var keyValue = allKeyValueMap[key];
+
+      if (keyValue.every(function (value) {
+        return !isNaN(Number(value));
+      })) {
+        code = [data[key]];
       }
+    } else {
+      Object.keys(allKeyValueMap).forEach(function (key) {
+        var keyValue = data[key];
+        var allKeyValue = allKeyValueMap[key];
+        var valueIndex = allKeyValue.findIndex(function (value) {
+          return keyValue === value;
+        });
+        var subCode = [];
 
-      code = code.concat(subCode);
-    });
+        for (var i = 0; i < allKeyValue.length; i++) {
+          if (i === valueIndex) {
+            subCode.push(1);
+          } else {
+            subCode.push(0);
+          }
+        }
+
+        code = code.concat(subCode);
+      });
+    }
+
     oneHotCode[index] = code;
@@ -88,0 +107,0 @@ });

lib/utils/node-properties.d.ts

 import { NodeConfig } from '../types';
 export declare const getAllSortProperties: (nodes?: NodeConfig[], n?: number) => string[];
 export declare const getPropertyWeight: (nodes: NodeConfig[]) => any[];
-export declare const getAllProperties: (nodes: any, key?: string) => any[];
+export declare const getAllProperties: (nodes: any, key?: any) => any[];
 declare const _default: {
     getAllSortProperties: (nodes?: NodeConfig[], n?: number) => string[];
     getPropertyWeight: (nodes: NodeConfig[]) => any[];
-    getAllProperties: (nodes: any, key?: string) => any[];
+    getAllProperties: (nodes: any, key?: any) => any[];
 };
 export default _default;

lib/utils/node-properties.js

@@ -88,3 +88,3 @@ "use strict";
   if (key === void 0) {
-    key = 'properties';
+    key = undefined;
   }
@@ -94,7 +94,9 @@
   nodes.forEach(function (node) {
-    if (!node.properties) {
-      return;
+    if (key === undefined) {
+      allProperties.push(node);
     }
 
-    allProperties.push(node[key]);
+    if (node[key] !== undefined) {
+      allProperties.push(node[key]);
+    }
   });
@@ -101,0 +103,0 @@ return allProperties;

lib/workers/algorithm.js

 "use strict";
 
-function _typeof(obj) { "@babel/helpers - typeof"; if (typeof Symbol === "function" && typeof Symbol.iterator === "symbol") { _typeof = function _typeof(obj) { return typeof obj; }; } else { _typeof = function _typeof(obj) { return obj && typeof Symbol === "function" && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj; }; } return _typeof(obj); }
+function _typeof(obj) { "@babel/helpers - typeof"; return _typeof = "function" == typeof Symbol && "symbol" == typeof Symbol.iterator ? function (obj) { return typeof obj; } : function (obj) { return obj && "function" == typeof Symbol && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj; }, _typeof(obj); }
 
@@ -5,0 +5,0 @@ Object.defineProperty(exports, "__esModule", {

lib/workers/index.worker.js

 "use strict";
 
-function _typeof(obj) { "@babel/helpers - typeof"; if (typeof Symbol === "function" && typeof Symbol.iterator === "symbol") { _typeof = function _typeof(obj) { return typeof obj; }; } else { _typeof = function _typeof(obj) { return obj && typeof Symbol === "function" && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj; }; } return _typeof(obj); }
+function _typeof(obj) { "@babel/helpers - typeof"; return _typeof = "function" == typeof Symbol && "symbol" == typeof Symbol.iterator ? function (obj) { return typeof obj; } : function (obj) { return obj && "function" == typeof Symbol && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj; }, _typeof(obj); }
 
@@ -5,0 +5,0 @@ Object.defineProperty(exports, "__esModule", {

package.json

 {
   "name": "@antv/algorithm",
-  "version": "0.1.21",
+  "version": "0.1.22",
   "description": "graph algorithm",
@@ -5,0 +5,0 @@ "keywords": [

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