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undirected-graph-typed

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undirected-graph-typed

Undirected Graph. Javascript & Typescript Data Structure.

  • 1.48.4
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  • npm
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Version published
Weekly downloads
139
decreased by-64.18%
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1
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NPM GitHub top language npm eslint npm package minimized gzipped size (select exports) npm bundle size npm

What

Brief

This is a standalone Undirected Graph data structure from the data-structure-typed collection. If you wish to access more data structures or advanced features, you can transition to directly installing the complete data-structure-typed package

How

install

npm

npm i undirected-graph-typed --save

yarn

yarn add undirected-graph-typed

methods

snippet

TS
JS

API docs & Examples

API Docs

Live Examples

Examples Repository

Data Structures

Data StructureUnit TestPerformance TestAPI Docs
Binary TreeBinary Tree
Binary Search Tree (BST)BST
AVL TreeAVLTree
Red Black TreeRedBlackTree
Tree MultisetTreeMultimap
Segment TreeSegmentTree
Binary Indexed TreeBinaryIndexedTree
HeapHeap
Priority QueuePriorityQueue
Max Priority QueueMaxPriorityQueue
Min Priority QueueMinPriorityQueue
TrieTrie
GraphAbstractGraph
Directed GraphDirectedGraph
Undirected GraphUndirectedGraph
QueueQueue
DequeDeque
Linked ListSinglyLinkedList
Singly Linked ListSinglyLinkedList
Doubly Linked ListDoublyLinkedList
StackStack

Standard library data structure comparison

Data Structure TypedC++ STLjava.utilPython collections
Heap<E>priority_queue<T>PriorityQueue<E>heapq
Deque<E>deque<T>ArrayDeque<E>deque
Queue<E>queue<T>Queue<E>-
HashMap<K, V>unordered_map<K, V>HashMap<K, V>defaultdict
DoublyLinkedList<E>list<T>LinkedList<E>-
SinglyLinkedList<E>---
BinaryTree<K, V>---
BST<K, V>---
RedBlackTree<E>set<T>TreeSet<E>-
RedBlackTree<K, V>map<K, V>TreeMap<K, V>-
TreeMultimap<K, V>multimap<K, V>--
-multiset<T>--
Trie---
DirectedGraph<V, E>---
UndirectedGraph<V, E>---
PriorityQueue<E>priority_queue<T>PriorityQueue<E>-
Array<E>vector<T>ArrayList<E>list
Stack<E>stack<T>Stack<E>-
Set<E>-HashSet<E>set
Map<K, V>-HashMap<K, V>dict
-unordered_set<T>HashSet<E>-
Map<K, V>--OrderedDict
-unordered_multiset-Counter
--LinkedHashSet<E>-
HashMap<K, V>-LinkedHashMap<K, V>-
-unordered_multimap<K, V>--
-bitset<N>--

Benchmark

avl-tree
test nametime taken (ms)executions per secsample deviation
10,000 add randomly31.3231.933.67e-4
10,000 add & delete randomly70.9014.100.00
10,000 addMany40.5824.644.87e-4
10,000 get27.3136.622.00e-4
binary-tree
test nametime taken (ms)executions per secsample deviation
1,000 add randomly12.3580.997.17e-5
1,000 add & delete randomly15.9862.587.98e-4
1,000 addMany10.9691.270.00
1,000 get18.6153.730.00
1,000 dfs164.206.090.04
1,000 bfs58.8417.000.01
1,000 morris256.663.907.70e-4
bst
test nametime taken (ms)executions per secsample deviation
10,000 add randomly31.5931.662.74e-4
10,000 add & delete randomly74.5613.418.32e-4
10,000 addMany29.1634.300.00
10,000 get29.2434.210.00
rb-tree
test nametime taken (ms)executions per secsample deviation
100,000 add85.8511.650.00
100,000 add & delete randomly211.544.730.00
100,000 getNode37.9226.371.65e-4
comparison
test nametime taken (ms)executions per secsample deviation
SRC PQ 10,000 add0.571748.734.96e-6
CJS PQ 10,000 add0.571746.694.91e-6
MJS PQ 10,000 add0.571749.684.43e-6
SRC PQ 10,000 add & pop3.47288.146.38e-4
CJS PQ 10,000 add & pop3.39295.363.90e-5
MJS PQ 10,000 add & pop3.37297.173.03e-5
directed-graph
test nametime taken (ms)executions per secsample deviation
1,000 addVertex0.109534.938.72e-7
1,000 addEdge6.30158.670.00
1,000 getVertex0.052.16e+43.03e-7
1,000 getEdge22.3144.820.00
tarjan210.904.740.01
tarjan all214.724.660.01
topologicalSort172.525.800.00
hash-map
test nametime taken (ms)executions per secsample deviation
1,000,000 set275.883.620.12
1,000,000 Map set211.664.720.01
1,000,000 Set add177.725.630.02
1,000,000 set & get317.603.150.02
1,000,000 Map set & get274.993.640.03
1,000,000 Set add & has172.235.810.02
1,000,000 ObjKey set & get929.401.080.07
1,000,000 Map ObjKey set & get310.023.230.05
1,000,000 Set ObjKey add & has283.283.530.04
heap
test nametime taken (ms)executions per secsample deviation
10,000 add & pop5.80172.358.78e-5
10,000 fib add & pop357.922.790.00
doubly-linked-list
test nametime taken (ms)executions per secsample deviation
1,000,000 push221.574.510.03
1,000,000 unshift229.024.370.07
1,000,000 unshift & shift169.215.910.02
1,000,000 insertBefore314.483.180.07
singly-linked-list
test nametime taken (ms)executions per secsample deviation
10,000 push & pop212.984.700.01
10,000 insertBefore250.683.990.01
max-priority-queue
test nametime taken (ms)executions per secsample deviation
10,000 refill & poll8.91112.292.26e-4
priority-queue
test nametime taken (ms)executions per secsample deviation
100,000 add & pop103.599.650.00
deque
test nametime taken (ms)executions per secsample deviation
1,000,000 push14.5568.726.91e-4
1,000,000 push & pop23.4042.735.94e-4
1,000,000 push & shift24.4140.971.45e-4
1,000,000 unshift & shift22.5644.321.30e-4
queue
test nametime taken (ms)executions per secsample deviation
1,000,000 push39.9025.070.01
1,000,000 push & shift81.7912.230.00
stack
test nametime taken (ms)executions per secsample deviation
1,000,000 push37.6026.600.00
1,000,000 push & pop47.0121.270.00
trie
test nametime taken (ms)executions per secsample deviation
100,000 push45.9721.760.00
100,000 getWords66.2015.110.00

Built-in classic algorithms

AlgorithmFunction DescriptionIteration Type
Binary Tree DFSTraverse a binary tree in a depth-first manner, starting from the root node, first visiting the left subtree, and then the right subtree, using recursion. Recursion + Iteration
Binary Tree BFSTraverse a binary tree in a breadth-first manner, starting from the root node, visiting nodes level by level from left to right. Iteration
Graph DFSTraverse a graph in a depth-first manner, starting from a given node, exploring along one path as deeply as possible, and backtracking to explore other paths. Used for finding connected components, paths, etc. Recursion + Iteration
Binary Tree MorrisMorris traversal is an in-order traversal algorithm for binary trees with O(1) space complexity. It allows tree traversal without additional stack or recursion. Iteration
Graph BFSTraverse a graph in a breadth-first manner, starting from a given node, first visiting nodes directly connected to the starting node, and then expanding level by level. Used for finding shortest paths, etc. Recursion + Iteration
Graph Tarjan's AlgorithmFind strongly connected components in a graph, typically implemented using depth-first search.Recursion
Graph Bellman-Ford AlgorithmFinding the shortest paths from a single source, can handle negative weight edgesIteration
Graph Dijkstra's AlgorithmFinding the shortest paths from a single source, cannot handle negative weight edgesIteration
Graph Floyd-Warshall AlgorithmFinding the shortest paths between all pairs of nodesIteration
Graph getCyclesFind all cycles in a graph or detect the presence of cycles.Recursion
Graph getCutVertexesFind cut vertices in a graph, which are nodes that, when removed, increase the number of connected components in the graph. Recursion
Graph getSCCsFind strongly connected components in a graph, which are subgraphs where any two nodes can reach each other. Recursion
Graph getBridgesFind bridges in a graph, which are edges that, when removed, increase the number of connected components in the graph. Recursion
Graph topologicalSortPerform topological sorting on a directed acyclic graph (DAG) to find a linear order of nodes such that all directed edges go from earlier nodes to later nodes. Recursion

Software Engineering Design Standards

PrincipleDescription
PracticalityFollows ES6 and ESNext standards, offering unified and considerate optional parameters, and simplifies method names.
ExtensibilityAdheres to OOP (Object-Oriented Programming) principles, allowing inheritance for all data structures.
ModularizationIncludes data structure modularization and independent NPM packages.
EfficiencyAll methods provide time and space complexity, comparable to native JS performance.
MaintainabilityFollows open-source community development standards, complete documentation, continuous integration, and adheres to TDD (Test-Driven Development) patterns.
TestabilityAutomated and customized unit testing, performance testing, and integration testing.
PortabilityPlans for porting to Java, Python, and C++, currently achieved to 80%.
ReusabilityFully decoupled, minimized side effects, and adheres to OOP.
SecurityCarefully designed security for member variables and methods. Read-write separation. Data structure software does not need to consider other security aspects.
ScalabilityData structure software does not involve load issues.

Keywords

FAQs

Package last updated on 06 Dec 2023

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