!NPM
!GitHub top language
!npm
!eslint
!npm bundle size
!npm bundle size
!npm

What

Brief

This is a standalone Directed 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

$3

``bash npm i directed-graph-typed --save`

`$3`

`bash yarn add directed-graph-typed`

`$3`

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

`$3`

typescript
 // Create a simple directed graph
    const graph = new DirectedGraph();
    // Add vertices
    graph.addVertex('A');
    graph.addVertex('B');
    graph.addVertex('C');
    // Verify vertices exist
    console.log(graph.hasVertex('A')); // true;
    console.log(graph.hasVertex('B')); // true;
    console.log(graph.hasVertex('C')); // true;
    console.log(graph.hasVertex('D')); // false;

// Check vertex count console.log(graph.size); // 3;`

`$3`

typescript
 const graph = new DirectedGraph();
    // Add vertices
    graph.addVertex('A');
    graph.addVertex('B');
    graph.addVertex('C');
    // Add directed edges
    graph.addEdge('A', 'B', 1);
    graph.addEdge('B', 'C', 2);
    graph.addEdge('A', 'C', 3);
    // Verify edges exist
    console.log(graph.hasEdge('A', 'B')); // true;
    console.log(graph.hasEdge('B', 'C')); // true;
    console.log(graph.hasEdge('C', 'B')); // false; // Graph is directed

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

`$3`

typescript
 const graph = new DirectedGraph();
    // Build a small graph
    graph.addVertex('X');
    graph.addVertex('Y');
    graph.addVertex('Z');
    graph.addEdge('X', 'Y', 1);
    graph.addEdge('Y', 'Z', 2);
    // Delete an edge
    graph.deleteEdgeSrcToDest('X', 'Y');
    console.log(graph.hasEdge('X', 'Y')); // false;
    // Edge in other direction should not exist
    console.log(graph.hasEdge('Y', 'X')); // false;
    // Other edges should remain
    console.log(graph.hasEdge('Y', 'Z')); // true;

// Delete a vertex graph.deleteVertex('Y'); console.log(graph.hasVertex('Y')); // false; console.log(graph.size); // 2;`

`$3`

typescript
 const graph = new DirectedGraph();
    // Build a DAG (Directed Acyclic Graph) for task dependencies
    graph.addVertex('Design');
    graph.addVertex('Implement');
    graph.addVertex('Test');
    graph.addVertex('Deploy');
    // Add dependency edges
    graph.addEdge('Design', 'Implement', 1); // Design must come before Implement
    graph.addEdge('Implement', 'Test', 1); // Implement must come before Test
    graph.addEdge('Test', 'Deploy', 1); // Test must come before Deploy
    // Topological sort gives valid execution order
    const executionOrder = graph.topologicalSort();
    console.log(executionOrder); // defined;
    console.log(executionOrder); // ['Design', 'Implement', 'Test', 'Deploy'];

// All vertices should be included console.log(executionOrder?.length); // 4;`

`$3`

typescript
 // Build a weighted directed graph representing network nodes and costs
    const network = new DirectedGraph();
    // Add network nodes
    network.addVertex('Router-A');
    network.addVertex('Router-B');
    network.addVertex('Router-C');
    network.addVertex('Router-D');
    network.addVertex('Router-E');
    // Add weighted edges (network latency costs)
    network.addEdge('Router-A', 'Router-B', 5);
    network.addEdge('Router-A', 'Router-C', 10);
    network.addEdge('Router-B', 'Router-D', 3);
    network.addEdge('Router-C', 'Router-D', 2);
    network.addEdge('Router-D', 'Router-E', 4);
    network.addEdge('Router-B', 'Router-E', 12);
    // Find shortest path from Router-A to Router-E
    const { minDist, minPath } = network.dijkstra('Router-A', 'Router-E', true, true) || {
      minDist: undefined,
      minPath: undefined
    };
    // Verify shortest path is found
    console.log(minDist); // defined;
    console.log(minPath); // defined;
    // Shortest path should be A -> B -> D -> E with cost 5+3+4=12
    // Or A -> C -> D -> E with cost 10+2+4=16
    // So the minimum is 12
    console.log(minDist); // <= 16;

// Verify path is valid (includes start and end) console.log(minPath?.[0].key); // 'Router-A'; console.log(minPath?.[minPath.length - 1].key); // 'Router-E';``

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

API docs & Examples

API Docs

Live Examples

Examples Repository

Data Structures

Data Structure	Unit Test	Performance Test	API Docs
Directed Graph			DirectedGraph

Standard library data structure comparison

Data Structure Typed	C++ STL	java.util	Python collections
DirectedGraph<V, E>	-	-	-

Benchmark

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

directed-graph

test name	time taken (ms)	executions per sec	sample deviation
1,000 addVertex	0.10	9534.93	8.72e-7
1,000 addEdge	6.30	158.67	0.00
1,000 getVertex	0.05	2.16e+4	3.03e-7
1,000 getEdge	22.31	44.82	0.00
tarjan	210.90	4.74	0.01
tarjan all	214.72	4.66	0.01
topologicalSort	172.52	5.80	0.00

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

Built-in classic algorithms

Algorithm	Function Description	Iteration Type
Graph DFS	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.	Recursion + Iteration
Graph BFS	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.	Recursion + Iteration
Graph Tarjan's Algorithm	Find strongly connected components in a graph, typically implemented using depth-first search.	Recursion
Graph Bellman-Ford Algorithm	Finding the shortest paths from a single source, can handle negative weight edges	Iteration
Graph Dijkstra's Algorithm	Finding the shortest paths from a single source, cannot handle negative weight edges	Iteration
Graph Floyd-Warshall Algorithm	Finding the shortest paths between all pairs of nodes	Iteration
Graph getCycles	Find all cycles in a graph or detect the presence of cycles.	Recursion
Graph getCutVertexes	Find cut vertices in a graph, which are nodes that, when removed, increase the number of connected components in the graph.	Recursion
Graph getSCCs	Find strongly connected components in a graph, which are subgraphs where any two nodes can reach each other.	Recursion
Graph getBridges	Find bridges in a graph, which are edges that, when removed, increase the number of connected components in the graph.	Recursion
Graph topologicalSort	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.	Recursion

Software Engineering Design Standards

Principle	Description
Practicality	Follows ES6 and ESNext standards, offering unified and considerate optional parameters, and simplifies method names.
Extensibility	Adheres to OOP (Object-Oriented Programming) principles, allowing inheritance for all data structures.
Modularization	Includes data structure modularization and independent NPM packages.
Efficiency	All methods provide time and space complexity, comparable to native JS performance.
Maintainability	Follows open-source community development standards, complete documentation, continuous integration, and adheres to TDD (Test-Driven Development) patterns.
Testability	Automated and customized unit testing, performance testing, and integration testing.
Portability	Plans for porting to Java, Python, and C++, currently achieved to 80%.
Reusability	Fully decoupled, minimized side effects, and adheres to OOP.
Security	Carefully designed security for member variables and methods. Read-write separation. Data structure software does not need to consider other security aspects.
Scalability	Data structure software does not involve load issues.

!NPM
!GitHub top language
!npm
!eslint
!npm bundle size
!npm bundle size
!npm

What

Brief

How

install

$3

``bash npm i directed-graph-typed --save`

`$3`

`bash yarn add directed-graph-typed`

`$3`

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

`$3`

typescript
 // Create a simple directed graph
    const graph = new DirectedGraph();
    // Add vertices
    graph.addVertex('A');
    graph.addVertex('B');
    graph.addVertex('C');
    // Verify vertices exist
    console.log(graph.hasVertex('A')); // true;
    console.log(graph.hasVertex('B')); // true;
    console.log(graph.hasVertex('C')); // true;
    console.log(graph.hasVertex('D')); // false;

// Check vertex count console.log(graph.size); // 3;`

`$3`

typescript
 const graph = new DirectedGraph();
    // Add vertices
    graph.addVertex('A');
    graph.addVertex('B');
    graph.addVertex('C');
    // Add directed edges
    graph.addEdge('A', 'B', 1);
    graph.addEdge('B', 'C', 2);
    graph.addEdge('A', 'C', 3);
    // Verify edges exist
    console.log(graph.hasEdge('A', 'B')); // true;
    console.log(graph.hasEdge('B', 'C')); // true;
    console.log(graph.hasEdge('C', 'B')); // false; // Graph is directed

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

`$3`

typescript
 const graph = new DirectedGraph();
    // Build a small graph
    graph.addVertex('X');
    graph.addVertex('Y');
    graph.addVertex('Z');
    graph.addEdge('X', 'Y', 1);
    graph.addEdge('Y', 'Z', 2);
    // Delete an edge
    graph.deleteEdgeSrcToDest('X', 'Y');
    console.log(graph.hasEdge('X', 'Y')); // false;
    // Edge in other direction should not exist
    console.log(graph.hasEdge('Y', 'X')); // false;
    // Other edges should remain
    console.log(graph.hasEdge('Y', 'Z')); // true;

// Delete a vertex graph.deleteVertex('Y'); console.log(graph.hasVertex('Y')); // false; console.log(graph.size); // 2;`

`$3`

typescript
 const graph = new DirectedGraph();
    // Build a DAG (Directed Acyclic Graph) for task dependencies
    graph.addVertex('Design');
    graph.addVertex('Implement');
    graph.addVertex('Test');
    graph.addVertex('Deploy');
    // Add dependency edges
    graph.addEdge('Design', 'Implement', 1); // Design must come before Implement
    graph.addEdge('Implement', 'Test', 1); // Implement must come before Test
    graph.addEdge('Test', 'Deploy', 1); // Test must come before Deploy
    // Topological sort gives valid execution order
    const executionOrder = graph.topologicalSort();
    console.log(executionOrder); // defined;
    console.log(executionOrder); // ['Design', 'Implement', 'Test', 'Deploy'];

// All vertices should be included console.log(executionOrder?.length); // 4;`

`$3`

typescript
 // Build a weighted directed graph representing network nodes and costs
    const network = new DirectedGraph();
    // Add network nodes
    network.addVertex('Router-A');
    network.addVertex('Router-B');
    network.addVertex('Router-C');
    network.addVertex('Router-D');
    network.addVertex('Router-E');
    // Add weighted edges (network latency costs)
    network.addEdge('Router-A', 'Router-B', 5);
    network.addEdge('Router-A', 'Router-C', 10);
    network.addEdge('Router-B', 'Router-D', 3);
    network.addEdge('Router-C', 'Router-D', 2);
    network.addEdge('Router-D', 'Router-E', 4);
    network.addEdge('Router-B', 'Router-E', 12);
    // Find shortest path from Router-A to Router-E
    const { minDist, minPath } = network.dijkstra('Router-A', 'Router-E', true, true) || {
      minDist: undefined,
      minPath: undefined
    };
    // Verify shortest path is found
    console.log(minDist); // defined;
    console.log(minPath); // defined;
    // Shortest path should be A -> B -> D -> E with cost 5+3+4=12
    // Or A -> C -> D -> E with cost 10+2+4=16
    // So the minimum is 12
    console.log(minDist); // <= 16;

// Verify path is valid (includes start and end) console.log(minPath?.[0].key); // 'Router-A'; console.log(minPath?.[minPath.length - 1].key); // 'Router-E';``

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

API docs & Examples

API Docs

Live Examples

Examples Repository

Data Structures

Data Structure	Unit Test	Performance Test	API Docs
Directed Graph			DirectedGraph

Standard library data structure comparison

Data Structure Typed	C++ STL	java.util	Python collections
DirectedGraph<V, E>	-	-	-

Benchmark

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

directed-graph

test name	time taken (ms)	executions per sec	sample deviation
1,000 addVertex	0.10	9534.93	8.72e-7
1,000 addEdge	6.30	158.67	0.00
1,000 getVertex	0.05	2.16e+4	3.03e-7
1,000 getEdge	22.31	44.82	0.00
tarjan	210.90	4.74	0.01
tarjan all	214.72	4.66	0.01
topologicalSort	172.52	5.80	0.00

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

Built-in classic algorithms

Algorithm	Function Description	Iteration Type
Graph DFS	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.	Recursion + Iteration
Graph BFS	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.	Recursion + Iteration
Graph Tarjan's Algorithm	Find strongly connected components in a graph, typically implemented using depth-first search.	Recursion
Graph Bellman-Ford Algorithm	Finding the shortest paths from a single source, can handle negative weight edges	Iteration
Graph Dijkstra's Algorithm	Finding the shortest paths from a single source, cannot handle negative weight edges	Iteration
Graph Floyd-Warshall Algorithm	Finding the shortest paths between all pairs of nodes	Iteration
Graph getCycles	Find all cycles in a graph or detect the presence of cycles.	Recursion
Graph getCutVertexes	Find cut vertices in a graph, which are nodes that, when removed, increase the number of connected components in the graph.	Recursion
Graph getSCCs	Find strongly connected components in a graph, which are subgraphs where any two nodes can reach each other.	Recursion
Graph getBridges	Find bridges in a graph, which are edges that, when removed, increase the number of connected components in the graph.	Recursion
Graph topologicalSort	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.	Recursion

Software Engineering Design Standards

Principle	Description
Practicality	Follows ES6 and ESNext standards, offering unified and considerate optional parameters, and simplifies method names.
Extensibility	Adheres to OOP (Object-Oriented Programming) principles, allowing inheritance for all data structures.
Modularization	Includes data structure modularization and independent NPM packages.
Efficiency	All methods provide time and space complexity, comparable to native JS performance.
Maintainability	Follows open-source community development standards, complete documentation, continuous integration, and adheres to TDD (Test-Driven Development) patterns.
Testability	Automated and customized unit testing, performance testing, and integration testing.
Portability	Plans for porting to Java, Python, and C++, currently achieved to 80%.
Reusability	Fully decoupled, minimized side effects, and adheres to OOP.
Security	Carefully designed security for member variables and methods. Read-write separation. Data structure software does not need to consider other security aspects.
Scalability	Data structure software does not involve load issues.