Bipartite Graph Check in DSA C# - Time & Space Complexity

Time Complexity: Bipartite Graph Check

O(n + m)

Understanding Time Complexity

Checking if a graph is bipartite means dividing nodes into two groups without edges inside the same group.

We want to know how the time needed grows as the graph gets bigger.

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

// Check if graph is bipartite using BFS
bool IsBipartite(List<int>[] graph) {
    int n = graph.Length;
    int[] colors = new int[n];
    Array.Fill(colors, -1);
    for (int i = 0; i < n; i++) {
        if (colors[i] == -1) {
            if (!BFSCheck(graph, i, colors)) return false;
        }
    }
    return true;
}

bool BFSCheck(List<int>[] graph, int start, int[] colors) {
    Queue<int> queue = new Queue<int>();
    queue.Enqueue(start);
    colors[start] = 0;
    while (queue.Count > 0) {
        int node = queue.Dequeue();
        foreach (int neighbor in graph[node]) {
            if (colors[neighbor] == -1) {
                colors[neighbor] = 1 - colors[node];
                queue.Enqueue(neighbor);
            } else if (colors[neighbor] == colors[node]) {
                return false;
            }
        }
    }
    return true;
}
    

This code checks if a graph can be colored using two colors without neighbors sharing the same color.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Visiting each node and its neighbors once using BFS.
• How many times: Each node and edge is checked at most once during BFS traversals.

How Execution Grows With Input

As the graph grows, the time grows with the number of nodes and edges.

Input Size (n nodes, m edges)	Approx. Operations
10 nodes, 15 edges	About 25 checks (nodes + edges)
100 nodes, 200 edges	About 300 checks
1000 nodes, 3000 edges	About 4000 checks

Pattern observation: The operations grow roughly in a straight line with nodes plus edges combined.

Final Time Complexity

Time Complexity: O(n + m)

This means the time needed grows linearly with the total number of nodes and edges in the graph.

Common Mistake

[X] Wrong: "The BFS inside the check runs for every node fully, so time is O(n * m)."

[OK] Correct: Each node and edge is visited only once overall, not repeatedly, so the total work is linear, not multiplied.

Interview Connect

Understanding this time complexity helps you explain how graph traversal scales and shows you can analyze common graph algorithms clearly.

Self-Check

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

Practice

1. What does it mean for a graph to be bipartite?

easy

A. All vertices have the same degree

B. It has an equal number of vertices and edges

C. It contains at least one cycle

D. Its vertices can be divided into two groups with no edges inside the same group

Solution

1. Step 1: Understand bipartite graph definition

   A bipartite graph is one where vertices can be split into two sets with edges only between sets, not inside a set.

2. Step 2: Compare options with definition

   Its vertices can be divided into two groups with no edges inside the same group matches this definition exactly, while others describe unrelated properties.

3. Final Answer:

   Its vertices can be divided into two groups with no edges inside the same group -> Option D

4. Quick Check:

   Bipartite graph = two groups, no internal edges [OK]

Hint: Bipartite means two groups with no edges inside groups [OK]

Common Mistakes:

• Confusing bipartite with equal vertices and edges
• Thinking bipartite means no cycles
• Assuming all vertices have same degree

2. Which C# data structure is best to represent a graph for bipartite check?

easy

A. List adjacency list

B. Dictionary adjacency list

C. int[,] adjacency matrix

D. HashSet<int> for vertices only

Solution

1. Step 1: Identify graph representation needs

   We need to store edges efficiently and access neighbors quickly for bipartite check.

2. Step 2: Evaluate options

   Dictionary allows flexible vertex keys and fast neighbor lookup, better than fixed-size arrays or sets.

3. Final Answer:

   Dictionary adjacency list -> Option B

4. Quick Check:

   Dictionary adjacency list = best for flexible graphs [OK]

Hint: Use Dictionary for flexible graph edges [OK]

Common Mistakes:

• Using HashSet only stores vertices, no edges
• Adjacency matrix wastes space for sparse graphs
• List lacks vertex key mapping

3. What is the output of this C# code snippet checking bipartite for a graph with edges: 0-1, 1-2, 2-3, 3-0?

var graph = new Dictionary {
  {0, new List<int>{1,3}},
  {1, new List<int>{0,2}},
  {2, new List<int>{1,3}},
  {3, new List<int>{2,0}}
};
bool IsBipartite(Dictionary g) {
  var color = new Dictionary();
  foreach(var node in g.Keys) {
    if(!color.ContainsKey(node)) {
      var queue = new Queue<int>();
      queue.Enqueue(node);
      color[node] = 0;
      while(queue.Count > 0) {
        var u = queue.Dequeue();
        foreach(var v in g[u]) {
          if(!color.ContainsKey(v)) {
            color[v] = 1 - color[u];
            queue.Enqueue(v);
          } else if(color[v] == color[u]) {
            return false;
          }
        }
      }
    }
  }
  return true;
}
Console.WriteLine(IsBipartite(graph));

medium

A. True

B. False

C. Compilation error

D. Runtime exception

Solution

1. Step 1: Analyze the graph structure

   The graph forms a cycle of length 4: 0-1-2-3-0.

2. Step 2: Check bipartite condition on even cycle

   Even cycles are bipartite, but here the cycle is length 4 (even), so it should be bipartite. However, the code returns false if adjacent nodes have same color.

3. Step 3: Trace coloring

   Coloring alternates 0 and 1 around the cycle, no conflict occurs, so function returns true.

4. Final Answer:

   True -> Option A

5. Quick Check:

   Even cycle graph is bipartite = True [OK]

Hint: Even cycles are bipartite; odd cycles are not [OK]

Common Mistakes:

• Assuming all cycles are not bipartite
• Confusing color assignment logic
• Ignoring queue BFS traversal

4. Identify the bug in this bipartite check code snippet:

bool CheckBipartite(Dictionary graph) {
  var color = new Dictionary();
  foreach(var node in graph.Keys) {
    if(!color.ContainsKey(node)) {
      color[node] = 0;
      var stack = new Stack<int>();
      stack.Push(node);
      while(stack.Count > 0) {
        var u = stack.Pop();
        foreach(var v in graph[u]) {
          if(!color.ContainsKey(v)) {
            color[v] = color[u];
            stack.Push(v);
          } else if(color[v] == color[u]) {
            return false;
          }
        }
      }
    }
  }
  return true;
}

medium

A. Colors assigned to neighbors are same as current node, should be opposite

B. Stack should be replaced with queue for BFS

C. Missing initialization of color dictionary

D. No return statement for true case

Solution

1. Step 1: Review color assignment logic

   Neighbors must have opposite colors to current node for bipartite check.

2. Step 2: Identify incorrect assignment

   Code assigns neighbor color same as current node, causing false negatives.

3. Final Answer:

   Colors assigned to neighbors are same as current node, should be opposite -> Option A

4. Quick Check:

   Neighbor colors must differ [OK]

Hint: Neighbors must have opposite colors, not same [OK]

Common Mistakes:

• Assigning same color to neighbors
• Confusing stack vs queue (both work but BFS preferred)
• Forgetting to initialize colors

5. Given a graph with vertices 0 to 5 and edges: 0-1, 1-2, 2-3, 3-4, 4-5, 5-0, and 1-4, is the graph bipartite? Explain why.

hard

A. Yes, because all cycles are even length

B. No, because it has a self-loop

C. No, because it contains an odd cycle including edges 1-2-3-4-1

D. Yes, because it has no cycles

Solution

1. Step 1: Identify cycles in the graph

   The graph has a large cycle 0-1-2-3-4-5-0 (length 6, even), and with edge 1-4, cycles 1-2-3-4-1 (length 4, even) and 0-1-4-5-0 (length 4, even).

2. Step 2: Check for odd-length cycles

   No odd-length cycles exist. A graph is bipartite if and only if it has no odd-length cycles.

3. Final Answer:

   No, because it contains an odd cycle including edges 1-2-3-4-1 -> Option C

4. Quick Check:

   Odd cycle means not bipartite [OK]

Hint: Check for odd cycles to decide bipartite [OK]

Common Mistakes:

• Miscounting cycle lengths
• Assuming any cycle means not bipartite
• Ignoring extra edges creating odd cycles