fast-graph

fast-graph

A robust and fast package for handling graph opperations and algorithms

Description

🚀 Cutting-edge TypeScript library designed to empower developers with a high-performance solution for efficient graph operations and algorithms. With a focus on speed and reliability, this library simplifies the implementation of graph-related tasks, offering a comprehensive set of features for seamless integration into any real world case.

Install

yarn add fast-graph

npm install fast-graph

Real life usecases

• Social Networks: Identify influencers and communities effortlessly.
• Software Dependencies: Streamline module sequencing in large projects.
• Route Optimization: Efficient planning for maps and network routing.
• Task Scheduling: Smooth management of project tasks and dependencies.
• Code Compilation: Parallel execution for faster builds.
• Recommendation Systems: Personalized user insights for better recommendations.
• Supply Chain Optimization: Improve logistics by analyzing distribution networks.

Usage

1. Node Class:

The Node class represents a node in the graph, holding a unique identifier (id) and an associated value of generic type (T). The incomingNeighbors property tracks incoming edges to the node.

import { Node } from "fast-graph";

// Example Usage:
const myNode = new Node<string>("uniqueId", "Node Value");

2. Graph Class:

The Graph class is the core component for graph operations. It allows you to create, manipulate, and perform various algorithms on graphs.

import { Node, Graph } from "fast-graph";

// Example Usage:
const myGraph = new Graph<string>();

// Adding Nodes
const nodeA = new Node<string>("A", "Node A");
const nodeB = new Node<string>("B", "Node B");
myGraph.addNode(nodeA);
myGraph.addNode(nodeB);

// Adding Edges
myGraph.addEdge(nodeA, nodeB);

// Removing Nodes
myGraph.removeNode(nodeA);

// Removing Edges
myGraph.removeEdge(nodeB, nodeA);

// Checking if the Graph is Empty
const isEmpty = myGraph.isEmpty();

// Getting Neighbors of a Node
const neighbors = myGraph.getNeighbors(nodeB);

// Kahn's Topological Sort
const topologicalOrder = myGraph.kahnTopologicalSort();

// Use BFS to visit all nodes in the graph
myGraph.bfs(node => {
  console.log(`Visiting Node ${node.id} with value ${node.value}`);
  return SearchAlgorithmNodeBehavior.continue;
});

// Use BFS to find a node
myGraph.bfs(node => {
  console.log(`Visiting Node ${node.id} with value ${node.value}`);
  return node.id === "id_your_looking_for"
    ? SearchAlgorithmNodeBehavior.break
    : SearchAlgorithmNodeBehavior.continue;
});

// Use BFS to visit all nodes in the graph async
await myGraph.bfsAsync(async node => {
  await yourExternalApiCall(node);

  return SearchAlgorithmNodeBehavior.continue;
});

// Use DFS to visit all nodes in the graph
myGraph.dfs(node => {
  console.log(`Visiting Node ${node.id} with value ${node.value}`);
  return SearchAlgorithmNodeBehavior.continue;
});

// Use DFS to find a node
myGraph.dfs(node => {
  console.log(`Visiting Node ${node.id} with value ${node.value}`);
  return node.id === "id_your_looking_for"
    ? SearchAlgorithmNodeBehavior.break
    : SearchAlgorithmNodeBehavior.continue;
});

// Use DFS to visit all nodes in the graph async
await myGraph.dfsAsync(async node => {
  await yourExternalApiCall(node);

  return SearchAlgorithmNodeBehavior.continue;
});

// Use Dijkstra's algorithm to discover shortest path from a node to all other nodes.
const dijkstraResult: NodeDistance = graph.dijkstra(nodeA);

Note: Ensure that you handle errors appropriately, as the library throws an error if attempting operations on non-existing nodes or in the presence of cycles.

Depth-First Search (DFS):

• Traversal:
  • DFS is commonly used for traversing or searching through a graph or tree data structure.
  • It explores as far as possible along each branch before backtracking.
• Connected Components:
  • DFS is used to identify connected components in an undirected graph.
• Cycle Detection:
  • DFS can be employed to detect cycles in a graph. If during the traversal, you encounter an already visited node, it indicates the presence of a cycle.
• Pathfinding:
  • DFS can help find paths between two nodes in a graph.
• Maze Solving:
  • DFS is useful for solving mazes and exploring possible paths.
• Backtracking:
  • It's a fundamental algorithm for backtracking problems.

Breadth-First Search (BFS):

• Shortest Path:
  • BFS is commonly used to find the shortest path between two nodes in an unweighted graph.
• Connected Components:
  • Similar to DFS, BFS can identify connected components in an undirected graph.
• Level Order Traversal:
  • BFS is effective for performing level-order traversal of a tree or graph.
• Maze Solving:
  • BFS can be used for maze-solving algorithms.
• Network Routing:
  • BFS is applied in network routing protocols to discover the shortest path.

Dijkstra's Algorithm:

• Shortest Paths:
  • Dijkstra's algorithm finds the shortest paths from a single source node to all other nodes in a graph with non-negative edge weights.
• Non-Negative Weights:
  • It is particularly suited for graphs with non-negative weights, providing accurate shortest paths.
• Applications:
  • Widely used in network routing, transportation systems, and any scenario where finding the shortest path is crucial.
• Priority Queue:
  • Dijkstra's algorithm utilizes a priority queue to efficiently select the next node with the smallest tentative distance.

Kahn's Topological Sort:

• Directed Acyclic Graph (DAG):
  • Kahn's algorithm is used for topological sorting of nodes in a Directed Acyclic Graph (DAG).
• Dependency Resolution:
  • In project management and build systems, Kahn's algorithm helps resolve dependencies between tasks or components.
• Course Scheduling:
  • In academic settings, Kahn's algorithm can be used for scheduling courses, ensuring prerequisites are met.
• Task Execution Order:
  • Kahn's algorithm is useful for determining the order of task execution when tasks have dependencies.
• Compiler Dependency Analysis:
  • In compilers, Kahn's algorithm aids in analyzing and resolving dependencies between modules.

General Considerations:

• Use DFS for...
  • Exploring all possible paths in a graph.
  • Cycle detection.
  • Backtracking problems.
• Use BFS for...
  • Finding the shortest path in an unweighted graph.
  • Level order traversal.
  • Maze solving.
• Use Dijkstra's Algorithm for...
  • Finding the shortest paths in graphs with non-negative weights.
  • Applications requiring accurate shortest paths.
• Use Kahn's Algorithm for...
  • Topological sorting in Directed Acyclic Graphs.
  • Dependency resolution in various applications.
  • Task scheduling and execution order determination.

Each algorithm has its strengths and is well-suited for specific types of problems. The choice depends on the nature of the problem you're solving and the characteristics of your data.

Contributing

Contributions are welcome! Please submit a pull request with any improvements or bug fixes. Make sure to add tests for any new features and bug fixes, and ensure that the existing tests pass.

License

This project is licensed under the MIT License.

Contact

If you need help or have questions, feel free to open an issue in the GitHub repository.