Java Program to Detect Cycle in a Directed Graph

Given a directed graph, check whether the graph contains a cycle. Your function should return true if the given graph contains at least one cycle; otherwise, it should return false.

Example:

Input: N = 4, E = 6

Output: Yes

Explanation: The diagram clearly shows a cycle 0 -> 2 -> 0

Input: N = 4, E = 4

Output: No

Explanation: The diagram clearly shows no cycle.

Approaches For Cycle Detection

There are two approaches to detect cycles in a directed graph:

• Depth-First Search (DFS)
• Kahn's Algorithm (Topological Sorting)

1. Java Program to Detect Cycle in a Directed Graph using (DFS)

The DFS approach is a popular method for cycle detection in directed graphs. The key idea is to use a recursive function to traverse the graph and maintain two arrays: one for visited nodes and another for nodes currently in the recursion stack (or "visiting" set). If we encounter a node that is already in the recursion stack, a cycle is detected.

• visited[]: To track whether a node has been visited.
• recStack[]: To keep track of nodes currently in the recursion stack.

Graph Class: Representing the Directed Graph

Algorithm to Create a Graph

1. Initialize the Graph
   • Input: Number of vertices, V.
   • Output: An empty graph with V vertices.
   • Steps:
     1. Create an adjacency list with V empty lists.
     2. Initialize each list to represent the neighbors of each vertex.
2. Add an Edge
   • Input: Source vertex src and destination vertex dest.
   • Output: Updated adjacency list with the edge added.
   • Steps:
     1. Append the destination vertex dest to the adjacency list of the source vertex src.

CycleDetection Class: Detecting Cycles

Algorithm to Detect Cycles using DFS

1. Depth-First Search (DFS)
   • Input: Current vertex v, arrays visited and recStack, graph object graph.
   • Output: True if a cycle is detected, otherwise False.
   • Steps:
     1. If recStack[v] is True, return True (cycle detected).
     2. If visited[v] is True, return False (no cycle through this vertex).
     3. Mark v as visited (visited[v] = True).
     4. Add v to the recursion stack (recStack[v] = True).
     5. For each neighbor n of vertex v:
        • Recursively call dfs(n, visited, recStack, graph).
        • If the recursive call returns True, return True (cycle detected).
     6. Remove v from the recursion stack (recStack[v] = False).
     7. Return False (no cycle detected through this path).
2. Cycle Detection
   • Input: Graph object graph.
   • Output: True if the graph contains a cycle, otherwise False.
   • Steps:
     1. Initialize arrays visited and recStack to False for all vertices.
     2. For each vertex v in the graph:
        • If visited[v] is False, call dfs(v, visited, recStack, graph).
        • If dfs returns True, return True (cycle detected).
     3. Return False (no cycle detected)

Implementation cycle detection using DFS in Java:

import java.util.ArrayList;
import java.util.List;

// Graph class representing a directed
// graph using adjacency list
class Graph {
    private final int vertices;
    private final List<List<Integer>> adjList;

    public Graph(int vertices) {
        this.vertices = vertices;
        adjList = new ArrayList<>(vertices);
        for (int i = 0; i < vertices; i++) {
            adjList.add(new ArrayList<>());
        }
    }

    public void addEdge(int src, int dest) {
        adjList.get(src).add(dest);
    }

    public List<Integer> getNeighbors(int vertex) {
        return adjList.get(vertex);
    }

    public int getVertices() {
        return vertices;
    }
}

// CycleDetection class to detect cycles in the graph
class CycleDetection {

    private boolean dfs(int vertex, boolean[] visited, boolean[] recStack, Graph graph) {
        if (recStack[vertex]) {
            return true;
        }
        if (visited[vertex]) {
            return false;
        }

        visited[vertex] = true;
        recStack[vertex] = true;

        for (int neighbor : graph.getNeighbors(vertex)) {
            if (dfs(neighbor, visited, recStack, graph)) {
                return true;
            }
        }

        recStack[vertex] = false;
        return false;
    }

    public boolean hasCycle(Graph graph) {
        int vertices = graph.getVertices();
        boolean[] visited = new boolean[vertices];
        boolean[] recStack = new boolean[vertices];

        for (int i = 0; i < vertices; i++) {
            if (!visited[i] && dfs(i, visited, recStack, graph)) {
                return true;
            }
        }

        return false;
    }
}

// Main class to demonstrate the cycle detection
public class Main {
    public static void main(String[] args) {
        Graph graph = new Graph(4);

        graph.addEdge(0, 1);
        graph.addEdge(1, 2);
        graph.addEdge(2, 0);
        graph.addEdge(2, 3);

        CycleDetection cycleDetection = new CycleDetection();
        boolean hasCycle = cycleDetection.hasCycle(graph);

        if (hasCycle) {
            System.out.println("The graph contains a cycle.");
        } else {
            System.out.println("The graph does not contain a cycle.");
        }
    }
}

The graph contains a cycle.

• Time Complexity: O(V + E), the Time Complexity of this method is the same as the time complexity of DFS traversal which is O(V+E).
• Auxiliary Space: O(V). To store the visited and recursion stack O(V) space is needed.

2. Java Program to Detect Cycle in a Directed Graph using Topological Sorting:

Kahn's algorithm can also be used to detect cycles. It involves calculating the in-degree (number of incoming edges) for each vertex. If at any point the number of processed vertices is less than the total number of vertices, a cycle exists.

Initialize the Graph

1. Input: Number of vertices V.
2. Output: An initialized graph with an adjacency list.
3. Procedure:
   • Create an adjacency list with V empty lists.
   • Initialize each list to represent the neighbors of each vertex.

Add an Edge

1. Input: Source vertex v and destination vertex w.
2. Output: Updated adjacency list with the edge added.
3. Procedure:
   • Append the destination vertex w to the adjacency list of the source vertex v.

Cycle Detection: Using Kahn's Algorithm (Topological Sorting)

Calculate In-Degree of Each Vertex

1. Input: The graph represented as an adjacency list.
2. Output: An array inDegree where inDegree[i] is the in-degree of vertex i.
3. Procedure:
   • Initialize an array inDegree of size V to store the in-degrees of all vertices.
   • Iterate over each vertex u in the graph.
   • For each adjacent vertex v of u, increment inDegree[v] by 1.

Enqueue Vertices with 0 In-Degree

1. Input: The array inDegree.
2. Output: A queue q containing vertices with 0 in-degree.
3. Procedure:
   • Initialize an empty queue q.
   • Iterate over each vertex u.
   • If inDegree[u] is 0, enqueue u into q.

Perform BFS and Check for Cycles

1. Input: The queue q, adjacency list adj, and array inDegree.
2. Output: A boolean value indicating if the graph contains a cycle.
3. Procedure:
   • Initialize a counter visited to count the number of visited vertices.
   • While the queue q is not empty:
     • Dequeue a vertex u from q.
     • Increment the visited counter.
     • For each adjacent vertex v of u:
       • Decrement inDegree[v] by 1.
       • If inDegree[v] becomes 0, enqueue v into q.
   • If the visited counter is not equal to the number of vertices V, return true (the graph contains a cycle).
   • Otherwise, return false.

Implementation cycle detection using Topological Sorting in Java:

// Java Program to detect cycle in a graph
import java.util.ArrayList;
import java.util.LinkedList;
import java.util.Queue;

class Graph {
    private int V; // number of vertices
    private ArrayList<ArrayList<Integer> >
        adj; // adjacency list

    public Graph(int V)
    {
        this.V = V;
        adj = new ArrayList<>(V);

        for (int i = 0; i < V; i++) {
            adj.add(new ArrayList<>());
        }
    }

    public void addEdge(int v, int w) { adj.get(v).add(w); }

    public boolean isCyclic()
    {
        int[] inDegree
            = new int[V]; // stores in-degree of each vertex
        Queue<Integer> q
            = new LinkedList<>(); // queue to store vertices
                                  // with 0 in-degree
        int visited = 0; // count of visited vertices

        // calculate in-degree of each vertex
        for (int u = 0; u < V; u++) {
            for (int v : adj.get(u)) {
                inDegree[v]++;
            }
        }

        // enqueue vertices with 0 in-degree
        for (int u = 0; u < V; u++) {
            if (inDegree[u] == 0) {
                q.add(u);
            }
        }

        // BFS traversal
        while (!q.isEmpty()) {
            int u = q.poll();
            visited++;

            // reduce in-degree of adjacent vertices
            for (int v : adj.get(u)) {
                inDegree[v]--;
                // if in-degree becomes 0, enqueue the
                // vertex
                if (inDegree[v] == 0) {
                    q.add(v);
                }
            }
        }

        return visited != V; // if not all vertices are
                             // visited, there is a cycle
    }
}
// Driver code
public class Main {
    public static void main(String[] args)
    {
        Graph g = new Graph(6);
        g.addEdge(0, 1);
        g.addEdge(0, 2);
        g.addEdge(1, 3);
        g.addEdge(4, 1);
        g.addEdge(4, 5);
        g.addEdge(5, 3);

        if (g.isCyclic()) {
            System.out.println("Graph contains cycle.");
        }
        else {
            System.out.println(
                "Graph does not contain cycle.");
        }
    }
}

Graph does not contain cycle.

Time Complexity: O(V + E), the time complexity of this method is the same as the time complexity of DFS traversal which is O(V+E).
Auxiliary Space: O(V). To store the visited and recursion stack O(V) space is needed.

kartik

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