undirected-graph-typed
Version:
Undirected Graph
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Markdown
# 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](https://www.npmjs.com/package/data-structure-typed) package
# How
## install
### npm
```bash
npm i undirected-graph-typed --save
```
### yarn
```bash
yarn add undirected-graph-typed
```
### snippet
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## API docs & Examples
[API Docs](https://data-structure-typed-docs.vercel.app)
[Live Examples](https://vivid-algorithm.vercel.app)
<a href="https://github.com/zrwusa/vivid-algorithm" target="_blank">Examples Repository</a>
## Data Structures
<table>
<thead>
<tr>
<th>Data Structure</th>
<th>Unit Test</th>
<th>Performance Test</th>
<th>API Docs</th>
</tr>
</thead>
<tbody>
<tr>
<td>Undirected Graph</td>
<td><img src="https://raw.githubusercontent.com/zrwusa/assets/master/images/data-structure-typed/assets/tick.svg" alt=""></td>
<td><img src="https://raw.githubusercontent.com/zrwusa/assets/master/images/data-structure-typed/assets/tick.svg" alt=""></td>
<td><a href="https://data-structure-typed-docs.vercel.app/classes/UndirectedGraph.html"><span>UndirectedGraph</span></a></td>
</tr>
</tbody>
</table>
## Standard library data structure comparison
<table>
<thead>
<tr>
<th>Data Structure Typed</th>
<th>C++ STL</th>
<th>java.util</th>
<th>Python collections</th>
</tr>
</thead>
<tbody>
<tr>
<td>UndirectedGraph<V, E></td>
<td>-</td>
<td>-</td>
<td>-</td>
</tr>
</tbody>
</table>
## Benchmark
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## Built-in classic algorithms
<table>
<thead>
<tr>
<th>Algorithm</th>
<th>Function Description</th>
<th>Iteration Type</th>
</tr>
</thead>
<tbody>
<tr>
<td>Graph DFS</td>
<td>Traverse 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.
</td>
<td>Recursion + Iteration</td>
</tr>
<tr>
<td>Graph BFS</td>
<td>Traverse 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.
</td>
<td>Recursion + Iteration</td>
</tr>
<tr>
<td>Graph Tarjan's Algorithm</td>
<td>Find strongly connected components in a graph, typically implemented using depth-first search.</td>
<td>Recursion</td>
</tr>
<tr>
<td>Graph Bellman-Ford Algorithm</td>
<td>Finding the shortest paths from a single source, can handle negative weight edges</td>
<td>Iteration</td>
</tr>
<tr>
<td>Graph Dijkstra's Algorithm</td>
<td>Finding the shortest paths from a single source, cannot handle negative weight edges</td>
<td>Iteration</td>
</tr>
<tr>
<td>Graph Floyd-Warshall Algorithm</td>
<td>Finding the shortest paths between all pairs of nodes</td>
<td>Iteration</td>
</tr>
<tr>
<td>Graph getCycles</td>
<td>Find all cycles in a graph or detect the presence of cycles.</td>
<td>Recursion</td>
</tr>
<tr>
<td>Graph getCutVertexes</td>
<td>Find cut vertices in a graph, which are nodes that, when removed, increase the number of connected components in
the graph.
</td>
<td>Recursion</td>
</tr>
<tr>
<td>Graph getSCCs</td>
<td>Find strongly connected components in a graph, which are subgraphs where any two nodes can reach each other.
</td>
<td>Recursion</td>
</tr>
<tr>
<td>Graph getBridges</td>
<td>Find bridges in a graph, which are edges that, when removed, increase the number of connected components in the
graph.
</td>
<td>Recursion</td>
</tr>
<tr>
<td>Graph topologicalSort</td>
<td>Perform 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.
</td>
<td>Recursion</td>
</tr>
</tbody>
</table>
## Software Engineering Design Standards
<table>
<tr>
<th>Principle</th>
<th>Description</th>
</tr>
<tr>
<td>Practicality</td>
<td>Follows ES6 and ESNext standards, offering unified and considerate optional parameters, and simplifies method names.</td>
</tr>
<tr>
<td>Extensibility</td>
<td>Adheres to OOP (Object-Oriented Programming) principles, allowing inheritance for all data structures.</td>
</tr>
<tr>
<td>Modularization</td>
<td>Includes data structure modularization and independent NPM packages.</td>
</tr>
<tr>
<td>Efficiency</td>
<td>All methods provide time and space complexity, comparable to native JS performance.</td>
</tr>
<tr>
<td>Maintainability</td>
<td>Follows open-source community development standards, complete documentation, continuous integration, and adheres to TDD (Test-Driven Development) patterns.</td>
</tr>
<tr>
<td>Testability</td>
<td>Automated and customized unit testing, performance testing, and integration testing.</td>
</tr>
<tr>
<td>Portability</td>
<td>Plans for porting to Java, Python, and C++, currently achieved to 80%.</td>
</tr>
<tr>
<td>Reusability</td>
<td>Fully decoupled, minimized side effects, and adheres to OOP.</td>
</tr>
<tr>
<td>Security</td>
<td>Carefully designed security for member variables and methods. Read-write separation. Data structure software does not need to consider other security aspects.</td>
</tr>
<tr>
<td>Scalability</td>
<td>Data structure software does not involve load issues.</td>
</tr>
</table>