undirected-graph-typed/README.md

Undirected Graph

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# 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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### basic UndirectedGraph vertex and edge creation
```typescript
 // Create a simple undirected graph
    const graph = new UndirectedGraph<string>();

    // Add vertices
    graph.addVertex('A');
    graph.addVertex('B');
    graph.addVertex('C');
    graph.addVertex('D');

    // Verify vertices exist
    console.log(graph.hasVertex('A')); // true;
    console.log(graph.hasVertex('B')); // true;
    console.log(graph.hasVertex('E')); // false;

    // Check vertex count
    console.log(graph.size); // 4;
```

### UndirectedGraph edge operations (bidirectional)
```typescript
 const graph = new UndirectedGraph<string>();

    // Add vertices
    graph.addVertex('A');
    graph.addVertex('B');
    graph.addVertex('C');

    // Add undirected edges (both directions automatically)
    graph.addEdge('A', 'B', 1);
    graph.addEdge('B', 'C', 2);
    graph.addEdge('A', 'C', 3);

    // Verify edges exist in both directions
    console.log(graph.hasEdge('A', 'B')); // true;
    console.log(graph.hasEdge('B', 'A')); // true; // Bidirectional!

    console.log(graph.hasEdge('C', 'B')); // true;
    console.log(graph.hasEdge('B', 'C')); // true; // Bidirectional!

    // Get neighbors of A
    const neighborsA = graph.getNeighbors('A');
    console.log(neighborsA[0].key); // 'B';
    console.log(neighborsA[1].key); // 'C';
```

### UndirectedGraph for social network connectivity analysis
```typescript
 interface Person {
      id: number;
      name: string;
      location: string;
    }

    // UndirectedGraph is perfect for modeling symmetric relationships
    // (friendships, collaborations, partnerships)
    const socialNetwork = new UndirectedGraph<number, Person>();

    // Add people as vertices
    const people: [number, Person][] = [
      [1, { id: 1, name: 'Alice', location: 'New York' }],
      [2, { id: 2, name: 'Bob', location: 'San Francisco' }],
      [3, { id: 3, name: 'Charlie', location: 'Boston' }],
      [4, { id: 4, name: 'Diana', location: 'New York' }],
      [5, { id: 5, name: 'Eve', location: 'Seattle' }]
    ];

    for (const [id] of people) {
      socialNetwork.addVertex(id);
    }

    // Add friendships (automatically bidirectional)
    socialNetwork.addEdge(1, 2, 1); // Alice <-> Bob
    socialNetwork.addEdge(1, 3, 1); // Alice <-> Charlie
    socialNetwork.addEdge(2, 4, 1); // Bob <-> Diana
    socialNetwork.addEdge(3, 5, 1); // Charlie <-> Eve
    socialNetwork.addEdge(4, 5, 1); // Diana <-> Eve

    console.log(socialNetwork.size); // 5;

    // Find direct connections for Alice
    const aliceConnections = socialNetwork.getNeighbors(1);
    console.log(aliceConnections[0].key); // 2;
    console.log(aliceConnections[1].key); // 3;
    console.log(aliceConnections.length); // 2;

    // Verify bidirectional connections
    console.log(socialNetwork.hasEdge(1, 2)); // true;
    console.log(socialNetwork.hasEdge(2, 1)); // true; // Friendship works both ways!

    // Remove a person from network
    socialNetwork.deleteVertex(2); // Bob leaves
    console.log(socialNetwork.hasVertex(2)); // false;
    console.log(socialNetwork.size); // 4;

    // Alice loses Bob as a friend
    const updatedAliceConnections = socialNetwork.getNeighbors(1);
    console.log(updatedAliceConnections[0].key); // 3;
    console.log(updatedAliceConnections[1]); // undefined;

    // Diana loses Bob as a friend
    const dianaConnections = socialNetwork.getNeighbors(4);
    console.log(dianaConnections[0].key); // 5;
    console.log(dianaConnections[1]); // undefined;
```

[//]: # (No deletion!!! End of Example Replace Section)



## 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&lt;V, E&gt;</td>
    <td>-</td>
    <td>-</td>
    <td>-</td>
  </tr>
 
  </tbody>
</table>

## Benchmark

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[//]: # (No deletion!!! End of Replace Section)

## 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>