My Algorithm Summary
  • Introduction
  • Data Structure
    • Linked List
    • Stack
      • Monotone Stack
        • 42 Trapping Rain Water
        • 84 Largest Rectangle in Histogram
        • 85 Maximal Rectangle
        • 255 Verify Preorder Sequence in Binary Search Tree
        • 316 Remove Duplicate Characters
        • 402 Remove K Digits
        • 456 132 Pattern
        • 496 Next Greater Element I
        • 503 Next Greater Element II
      • 20 Valid Parentheses
      • 71 Simplify Path
      • 150 Evaluate Reverse Polish Notation
      • 155 Min Stack
      • 173 Binary Search Tree Iterator
      • 224 Basic Calculator
      • 227 Basic Calculator II
      • 232 Implement Queue using Stacks
      • 341 Flatten Nested List Iterator
      • 394 Decode String
      • 439 Ternary Expression Parser
      • 636 Exclusive Time of Functions
    • Heap
    • Trie
    • Segment Tree
    • Tree
      • 94 Binary Tree Inorder Traversal
      • 104 Maximum Depth of Binary Tree
      • 144 Binary Tree Preorder Traversal
      • 145 Binary Tree Postorder Traversal
      • 199 Binary Tree Right Side View
      • 226 Invert Binary Tree
      • 272 Closest Binary Search Tree Value II
      • 508 Most Frequent Subtree Sum
      • 513 Find Bottom Left Tree Value
      • 515 Find Largest Value in Each Tree Row
      • 617 Merge Two Binary Trees
      • 637 Average of Levels in Binary Tree
      • 653 Two Sum IV - Input is a BST
      • 654 Maximum Binary Tree
      • 669 Trim a Binary Search Tree
      • 666 Path Sum IV
      • 230 Kth Smallest Element in a BST
      • 250 Count Univalue Subtrees
      • 538 Convert BST to Greater Tree
      • 404 Sum of Left Leaves
      • 582 Kill Process
      • 112 Path Sum
      • 108 Convert Sorted Array to Binary Search Tree
      • 111 Minimum Depth of Binary Tree
      • 501 Find Mode in Binary Search Tree
      • 102 Binary Tree Level Order Traversal
      • 107 Binary Tree Level Order Traversal II
      • 103 Binary Tree Zigzag Level Order Traversal
      • 113 Path Sum II
      • 437 Path Sum III
      • 99 Recover Binary Search Tree
      • 687 Longest Univalue Path
      • 285 Inorder Successor in BST
      • 101 Symmetric Tree
      • 129 Sum Root to Leaf Numbers
      • 298 Binary Tree Longest Consecutive Sequence
      • 270 Closest Binary Search Tree Value
      • 549 Binary Tree Longest Consecutive Sequence II
      • 98 Validate Binary Search Tree
      • 652 Find Duplicate Subtrees
      • 314 Binary Tree Vertical Order Traversal
      • 333 Largest BST Subtree
      • 563 Binary Tree Tilt
      • 110 Balanced Binary Tree
    • Graph
      • Detect Cycle
  • Algorithms
    • Union Find
      • 695 Max Area of Island
      • 684 Redundant Connection
    • Binary Search
    • Topological Sorting
    • Breadth-First Search
      • 694 Number of Distinct Islands
    • Depth-First Search
    • Two Pointers
    • Sorting
    • Backtacking
    • Dynamic Programming
      • Interval DP
        • Matrix Chain Multiplication
        • Merge Stone
      • KnapSack Problem
        • 0-1 KnapSack
        • Unbounded KnapSack
      • Longest Increasing Subsequence
      • Longest Common Subsequence
    • Reservior Sampling
    • Bipartite Graph
      • Check Bipartite Graph
      • Maximal Matching - Hungarian Algorithm
    • String Pattern Matching
      • KMP Algorithm
      • Rabin Karp Algorithm
  • System Design
    • Consistent Hashing
    • Bloom Filter
    • Caching
      • LRU
      • LFU
    • Mini Twitter
    • Tiny Url
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On this page
  • Cycle in Undirected Graph
  • 1. Implementation
  • 2. Time Complexity
  • Cycle in Directed Graph
  • 1. Detect Cycle with Coloring
  • 2. Time Complexity

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  1. Data Structure
  2. Graph

Detect Cycle

Cycle in Undirected Graph

1. Implementation

public static boolean hasCycle(int[][] edges, int n) {
        List<List<Integer>> adjList = new ArrayList<>();

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

        for (int[] edge : edges) {
            adjList.get(edge[0]).add(edge[1]);
            adjList.get(edge[1]).add(edge[0]);
        }

        boolean[] visited = new boolean[n];

        for (int i = 0; i < n; i++) {
            if (!visited[i]) {
                if (detectCycle(i, -1, visited, adjList)) {
                    return true;
                }
            }
        }
        return false;
    }

    public static boolean detectCycle(int curVertex, int parent, boolean[] visited, List<List<Integer>> adjList) {
        visited[curVertex] = true;

        for (int nextVertex : adjList.get(curVertex)) {
            if (!visited[nextVertex]) {
                if (detectCycle(nextVertex, curVertex, visited, adjList)) {
                    return true;
                }
            }
            // If an adjacent is visited and not parent of current vertex, then there is a cycle.
            else if (nextVertex != parent) {
                return true;
            }
        }
        return false;
    }

2. Time Complexity

Time: O(V + E)

Space: O(V + E)

Cycle in Directed Graph

1. Detect Cycle with Coloring

public enum Color {WHITE, GRAY, BLACK};

    public static boolean hasCycle(int[][] edges, int n) {
        List<List<Integer>> adjList = new ArrayList<>();

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

        for (int[] edge : edges) {
            adjList.get(edge[0]).add(edge[1]);
        }

        Color[] colors = new Color[n];
        Arrays.fill(colors, Color.WHITE);

        for (int i = 0; i < n; i++) {
            // vertex with White means this vertex is not visited yet
            if (colors[i] == Color.WHITE) {
                if (dfs(i, colors, adjList)) {
                    return true;
                }
            }
        }
        return false;
    }

    public static boolean dfs(int curVertex, Color[] colors, List<List<Integer>> adjList) {
        // vertex with Gray means it is in the process of DFS
        colors[curVertex] = Color.GRAY;

        for (int nextVertex : adjList.get(curVertex)) {
            if (colors[nextVertex] == Color.GRAY) {
                return true;
            }

            if (colors[nextVertex] == Color.WHITE && dfs(nextVertex, colors, adjList)) {
                return true;
            }
        }
        // vertex with Black means it is done for DFS
        colors[curVertex] = Color.BLACK;
        return false;
    }

2. Time Complexity

Time: O(V + E)

Space: O(V + E)

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Last updated 5 years ago

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