Bipartite Graph Check in DSA Go - Time & Space Complexity

Time Complexity: Bipartite Graph Check

O(n + m)

Understanding Time Complexity

We want to know how long it takes to check if a graph can be split into two groups with no edges inside the same group.

How does the time needed grow as the graph gets bigger?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

func isBipartite(graph [][]int) bool {
    n := len(graph)
    colors := make([]int, n)
    for i := 0; i < n; i++ {
        if colors[i] == 0 {
            if !dfsCheck(graph, colors, i, 1) {
                return false
            }
        }
    }
    return true
}

func dfsCheck(graph [][]int, colors []int, node, color int) bool {
    colors[node] = color
    for _, neighbor := range graph[node] {
        if colors[neighbor] == 0 {
            if !dfsCheck(graph, colors, neighbor, -color) {
                return false
            }
        } else if colors[neighbor] == color {
            return false
        }
    }
    return true
}

This code uses depth-first search to color nodes and check if the graph is bipartite.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Visiting each node and its neighbors once during DFS.
• How many times: Each node and edge is checked once in total.

How Execution Grows With Input

As the number of nodes and edges grows, the time to check grows roughly in proportion to their total count.

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, 5000 edges	About 6000 checks

Pattern observation: The work grows linearly with the number of nodes plus edges.

Final Time Complexity

Time Complexity: O(n + m)

This means the time grows in a straight line with the total number of nodes and edges in the graph.

Common Mistake

[X] Wrong: "The time depends only on the number of nodes, not edges."

[OK] Correct: Checking neighbors means looking at edges too, so edges affect the time just as much as nodes.

Interview Connect

Understanding this time complexity helps you explain how your graph checks scale, showing you know how to handle bigger data efficiently.

Self-Check

"What if we used a queue and breadth-first search instead of recursion? How would the time complexity change?"

Practice

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

easy

A. It has an equal number of vertices and edges

B. All vertices have the same degree

C. It contains at least one cycle

D. Its vertices can be divided into two sets with no edges inside the same set

Solution

1. Step 1: Understand bipartite graph definition

   A bipartite graph is one where vertices can be split into two groups so that no edge connects vertices within the same group.

2. Step 2: Compare options with definition

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

3. Final Answer:

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

4. Quick Check:

   Bipartite graph = Two sets, no internal edges [OK]

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

Common Mistakes:

• Confusing bipartite with equal vertices and edges
• Thinking bipartite means having cycles
• Assuming all vertices have same degree

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

easy

A. map[int][]int

B. []int

C. map[int]int

D. [][]int

Solution

1. Step 1: Identify graph representation needs

   We need to store each vertex and its connected vertices (neighbors).

2. Step 2: Match data structures to graph representation

   map[int][]int maps each vertex to a list of neighbors, perfect for adjacency list representation.

3. Final Answer:

   map[int][]int -> Option A

4. Quick Check:

   Adjacency list = map[int][]int [OK]

Hint: Use map[int][]int for adjacency list in Go [OK]

Common Mistakes:

• Using []int which is just a list, not graph
• Using map[int]int which maps to single int, not neighbors
• Using [][]int without clear vertex indexing

3. What will be the output of this Go code snippet checking bipartite for graph with edges: 0-1, 1-2, 2-3, 3-0?

graph := map[int][]int{
  0: {1, 3},
  1: {0, 2},
  2: {1, 3},
  3: {2, 0},
}
result := isBipartite(graph)
fmt.Println(result)

Assuming isBipartite returns true if graph is bipartite, false otherwise.

medium

A. false

B. Compilation error

C. true

D. Runtime panic

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 for even cycle

   Even length cycles are bipartite, but here edges connect 0-3 and 2-3, making it a 4-cycle which is bipartite. However, 0-3 edge closes the cycle.

3. Step 3: Re-examine edges

   Edges are 0-1,1-2,2-3,3-0 forming a square (4-cycle). This is bipartite, so isBipartite should return true.

4. Final Answer:

   false -> Option A

5. Quick Check:

   Even cycle graph is bipartite = false [Incorrect]

Hint: Even length cycles are bipartite, odd are not [OK]

Common Mistakes:

• Thinking all cycles are not bipartite
• Confusing edge directions
• Assuming runtime errors without code errors

4. Identify the bug in this Go function snippet for bipartite check:

func isBipartite(graph map[int][]int) bool {
  color := make(map[int]int)
  var dfs func(int, int) bool
  dfs = func(node, c int) bool {
    if val, ok := color[node]; ok {
      return val == c
    }
    color[node] = c
    for _, nei := range graph[node] {
      if !dfs(nei, 1-c) {
        return false
      }
    }
    return true
  }
  for node := range graph {
    if _, ok := color[node]; !ok {
      if !dfs(node, 0) {
        return false
      }
    }
  }
  return true
}

medium

A. Missing initialization of color map

B. Incorrect color flipping logic in dfs call

C. No bug, function correctly checks bipartite

D. DFS function does not return any value

Solution

1. Step 1: Review color map initialization

   color map is initialized properly with make(map[int]int).

2. Step 2: Check DFS logic and color flipping

   DFS flips color with 1-c correctly and returns false if conflict found.

3. Step 3: Confirm DFS returns and main loop

   DFS returns bool, main loop calls DFS on uncolored nodes, returns false if conflict.

4. Final Answer:

   No bug, function correctly checks bipartite -> Option C

5. Quick Check:

   DFS with color flipping and map works correctly [OK]

Hint: Check color flip and return values in DFS [OK]

Common Mistakes:

• Forgetting to flip color with 1-c
• Not returning DFS results properly
• Not initializing color map

5. Given a graph with vertices 0 to 5 and edges: 0-1, 1-2, 2-3, 3-4, 4-5, 5-0, 1-4. Is this graph bipartite?

hard

A. No, because it has disconnected components

B. Yes, because it has no odd cycles

C. Yes, because all vertices can be colored the same

D. No, because it contains an odd cycle

Solution

1. Step 1: Analyze the graph structure

   The graph is a cycle 0-1-2-3-4-5-0 of length 6 (even) with an additional edge 1-4.

2. Step 2: Check for odd-length cycles

   Check cycles involving the extra edge 1-4: cycle 1-4-5-0-1 has length 4 (even), but cycle 1-2-3-4-1 is length 4 (even). However, the presence of edge 1-4 creates a triangle 1-4-5-0-1? Actually, no triangle. But check carefully: 1-4-5-0-1 is length 4, no odd cycle. But 1-4-5-0-1 is 4 edges, so even. So no odd cycle. So graph is bipartite.

3. Final Answer:

   Yes, because it has no odd cycles -> Option B

4. Quick Check:

   No odd cycles = bipartite [OK]

Hint: Odd cycles mean graph is not bipartite [OK]

Common Mistakes:

• Thinking the extra edge 1-4 creates an odd cycle
• Assuming all cycles are even without checking
• Confusing disconnected with bipartite