Cycle Detection in Undirected Graph in DSA C - Time & Space Complexity

Time Complexity: Cycle Detection in Undirected Graph

O(V + E)

Understanding Time Complexity

We want to know how the time needed to find a cycle in an undirected graph changes as the graph grows.

How does the number of nodes and edges affect the work done?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

// Detect cycle using DFS in undirected graph
bool dfs(int node, int parent, vector<int> adj[], vector<bool> &visited) {
    visited[node] = true;
    for (int neighbor : adj[node]) {
        if (!visited[neighbor]) {
            if (dfs(neighbor, node, adj, visited))
                return true;
        } else if (neighbor != parent) {
            return true;
        }
    }
    return false;
}

bool isCycle(int V, vector<int> adj[]) {
    vector<bool> visited(V, false);
    for (int i = 0; i < V; i++) {
        if (!visited[i]) {
            if (dfs(i, -1, adj, visited))
                return true;
        }
    }
    return false;
}
    

This code checks if an undirected graph has a cycle by visiting nodes using depth-first search.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: DFS visits each node and explores all its neighbors.
• How many times: Each node and edge is visited once during the DFS traversal.

How Execution Grows With Input

As the graph grows, the time to check all nodes and edges grows roughly in proportion to their count.

Input Size (V = nodes, E = edges)	Approx. Operations
10 nodes, 15 edges	About 40 visits (nodes + edges)
100 nodes, 200 edges	About 500 visits
1000 nodes, 3000 edges	About 4000 visits

Pattern observation: The work grows roughly as the sum of nodes and edges, so doubling nodes and edges roughly doubles the work.

Final Time Complexity

Time Complexity: O(V + E)

This means the time grows linearly with the number of nodes plus the number of edges in the graph.

Common Mistake

[X] Wrong: "The DFS visits every edge multiple times, so the time is much higher than O(V + E)."

[OK] Correct: Each edge is checked only twice (once from each node), so the total work is still proportional to V + E, not more.

Interview Connect

Understanding this time complexity helps you explain how graph traversal scales and why DFS is efficient for cycle detection in undirected graphs.

Self-Check

"What if we used BFS instead of DFS for cycle detection? How would the time complexity change?"

Practice

1. What is the main idea behind detecting a cycle in an undirected graph using DFS?

easy

A. If the graph is disconnected, it contains a cycle.

B. If all nodes are visited exactly once, the graph has a cycle.

C. If we visit a node that is already visited and is not the parent, a cycle exists.

D. If the graph has more edges than nodes, it always has a cycle.

Solution

1. Step 1: Understand DFS traversal in undirected graph

   DFS visits nodes and tracks the parent node to avoid false cycle detection.

2. Step 2: Detect cycle condition

   If a visited node is found that is not the parent, it means a back edge exists, indicating a cycle.

3. Final Answer:

   If we visit a node that is already visited and is not the parent, a cycle exists. -> Option C

4. Quick Check:

   Cycle detection in undirected graph = check visited node not parent [OK]

Hint: Check visited neighbors excluding parent to find cycles [OK]

Common Mistakes:

• Ignoring the parent node in cycle check
• Assuming disconnected graph always has cycles
• Confusing edge count with cycle presence

2. Which of the following is the correct function signature in C for DFS used in cycle detection in an undirected graph?

easy

A. bool dfs(int node, int parent, int visited[], int graph[][100], int n)

B. int dfs(int node, int visited[], int graph[][100])

C. void dfs(int node, int graph[][100], int visited[])

D. int dfs(int node, int parent, int graph[][100])

Solution

1. Step 1: Identify parameters needed for DFS cycle detection

   We need current node, parent node, visited array, adjacency matrix, and number of nodes.

2. Step 2: Match function signature with parameters

   bool dfs(int node, int parent, int visited[], int graph[][100], int n) includes all required parameters with correct types and order.

3. Final Answer:

   bool dfs(int node, int parent, int visited[], int graph[][100], int n) -> Option A

4. Quick Check:

   DFS signature includes node, parent, visited, graph, size [OK]

Hint: Include parent parameter to avoid false cycle detection [OK]

Common Mistakes:

• Omitting parent parameter
• Wrong return type for DFS
• Missing graph size parameter

3. Consider the following adjacency matrix for an undirected graph with 4 nodes:

int graph[4][4] = {
  {0, 1, 0, 0},
  {1, 0, 1, 1},
  {0, 1, 0, 0},
  {0, 1, 0, 0}
};

If we run cycle detection using DFS starting from node 0, what will be the output?

medium

A. Cycle detected

B. No cycle detected

C. Runtime error due to invalid indexing

D. Infinite loop

Solution

1. Step 1: Analyze graph edges from adjacency matrix

   Edges: 0-1, 1-2, 1-3. Node 1 connects to nodes 0, 2, 3.

2. Step 2: Check for cycle using DFS

   Starting at 0, DFS visits 1, then 2 and 3. Node 1 connects back to 0 (parent), no cycle there. No back edge to visited non-parent node is found, so no cycle.

3. Final Answer:

   No cycle detected -> Option B

4. Quick Check:

   Graph edges form a tree structure = No cycle [OK]

Hint: Trace DFS and check visited nodes excluding parent [OK]

Common Mistakes:

• Assuming any connected graph has cycle
• Ignoring parent node in cycle check
• Confusing adjacency matrix indexing

4. The following C code snippet is intended to detect a cycle in an undirected graph using DFS. What is the bug?

bool dfs(int node, int parent, int visited[], int graph[][100], int n) {
  visited[node] = 1;
  for (int i = 0; i < n; i++) {
    if (graph[node][i] == 1) {
      if (!visited[i]) {
        if (dfs(i, node, visited, graph, n))
          return true;
      } else if (i != parent) {
        return true;
      }
    }
  }
  return false;
}

medium

A. The visited array is not initialized before calling dfs.

B. The parent parameter is not passed correctly in recursive calls.

C. The graph adjacency matrix is accessed out of bounds.

D. The function should return void, not bool.

Solution

1. Step 1: Check initialization of visited array

   The code snippet does not show visited array initialization, which is required to mark nodes unvisited before DFS.

2. Step 2: Verify other parameters and logic

   Parent is passed correctly, adjacency matrix access is within bounds, and return type bool is appropriate for cycle detection.

3. Final Answer:

   The visited array is not initialized before calling dfs. -> Option A

4. Quick Check:

   Initialize visited[] before DFS to avoid incorrect results [OK]

Hint: Always initialize visited array to zero before DFS [OK]

Common Mistakes:

• Forgetting to initialize visited array
• Passing wrong parent in recursion
• Misusing return type for DFS

5. You have an undirected graph represented by adjacency lists:

0: 1, 2
1: 0, 3
2: 0, 3
3: 1, 2

Using cycle detection with DFS, which nodes form the cycle and why?

hard

A. Nodes 0, 1, 3 form a cycle because 0 connects to 1 and 1 connects to 3.

B. Nodes 0, 2, 3 form a cycle because 0 connects to 2 and 2 connects to 3 which connects back to 0.

C. Nodes 1, 2, 3 form a cycle because 1 connects to 3 and 2 connects to 3.

D. Nodes 0, 1, 2, 3 form a cycle because they form a closed loop through all edges.

Solution

1. Step 1: Analyze adjacency lists for cycles

   Edges: 0-1, 0-2, 1-3, 2-3. These edges connect nodes forming a square shape.

2. Step 2: Identify cycle involving all nodes

   Starting at 0, path 0->1->3->2->0 forms a closed loop including all four nodes, indicating a cycle involving nodes 0,1,2,3.

3. Final Answer:

   Nodes 0, 1, 2, 3 form a cycle because they form a closed loop through all edges. -> Option D

4. Quick Check:

   Cycle includes all nodes forming closed loop [OK]

Hint: Look for closed loops connecting all nodes [OK]

Common Mistakes:

• Identifying partial cycles ignoring full loop
• Confusing connected nodes with cycle nodes
• Missing edges that complete the cycle