		export {default as betweenness} from './betweenness';
		export {default as closeness} from './closeness';
		export {default as degree} from './degree';
		@@ -3,0 +4,0 @@ export {default as eigenvector} from './eigenvector';

centrality/index.js

		@@ -8,2 +8,3 @@ /**
		exports.betweenness = require('./betweenness.js');
		exports.closeness = require('./closeness.js');
		exports.degree = require('./degree.js');
		@@ -10,0 +11,0 @@ exports.eigenvector = require('./eigenvector.js');

package.json

		{
		"name": "graphology-metrics",
		"version": "1.17.0",
		"version": "1.18.0",
		"description": "Miscellaneous graph metrics for graphology.",
		@@ -5,0 +5,0 @@ "main": "index.js",

README.md

		@@ -25,2 +25,3 @@ # Graphology metrics
		- [Betweenness centrality](#betweenness-centrality)
		- [Closeness centrality](#closeness-centrality)
		- [Degree centrality](#degree-centrality)
		@@ -220,2 +221,27 @@ - [Eigenvector centrality](#eigenvector-centrality)

		### Closeness centrality

		Computes the closeness centrality of a graph's nodes.

		```js
		import closenessCentrality from 'graphology-metrics/centrality/closeness';

		// To compute the eigenvector centrality and return the score per node:
		const scores = closenessCentrality(graph);

		// To directly map the result to nodes' attributes:
		closenessCentrality.assign(graph);

		// Note that you can also pass options to customize the algorithm:
		const p = closenessCentrality(graph, {wassermanFaust: true});
		```

		_Arguments_

		- graph _Graph_: target graph.
		- options _?object_: options:
		- attributes _?object_: attributes' names:
		- centrality _?string_ [`eigenvectorCentrality`]: name of the node attribute that will be assigned the eigenvector centrality.
		- wassermanFaust _?boolean_ [`false`]: whether to use Wasserman & Faust's normalization scheme.

		#### Degree centrality
		@@ -222,0 +248,0 @@

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