Bipartite Graph Check in DSA Rust - Time & Space Complexity

Time Complexity: Bipartite Graph Check

O(n + e)

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.

fn is_bipartite(graph: &Vec<Vec<usize>>) -> bool {
    let n = graph.len();
    let mut colors = vec![-1; n];
    for i in 0..n {
        if colors[i] == -1 {
            let mut stack = vec![i];
            colors[i] = 0;
            while let Some(node) = stack.pop() {
                for &neighbor in &graph[node] {
                    if colors[neighbor] == -1 {
                        colors[neighbor] = 1 - colors[node];
                        stack.push(neighbor);
                    } else if colors[neighbor] == colors[node] {
                        return false;
                    }
                }
            }
        }
    }
    true
}

This code checks if a graph is bipartite by coloring nodes using depth-first search.

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 at most once.

How Execution Grows With Input

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

Input Size (n nodes, e 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 roughly with the sum of nodes and edges.

Final Time Complexity

Time Complexity: O(n + e)

This means the time grows linearly 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: Because edges connect nodes, checking neighbors means looking at edges too, so edges affect the time.

Interview Connect

Understanding this time complexity helps you explain how graph traversal scales and shows you can analyze algorithms that explore connections efficiently.

Self-Check

"What if we used breadth-first search (BFS) instead of depth-first search (DFS)? How would the time complexity change?"

Practice

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

easy

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

B. It has an equal number of vertices and edges

C. It contains at least one cycle of odd length

D. All vertices have the same degree

Solution

1. Step 1: Understand bipartite graph definition

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

2. Step 2: Check options against definition

   Its vertices can be divided into two groups with no edges inside the same group matches this definition exactly. Options A, B, and D describe unrelated properties.

3. Final Answer:

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

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 graphs must have cycles
• Assuming all vertices have same degree

2. Which Rust code snippet correctly declares an adjacency list for an undirected graph to check bipartiteness?

easy

A. let graph: Vec<(usize, usize)> = Vec::new();

B. let graph: Vec<usize> = vec![0; n];

C. let graph: HashMap = HashMap::new();

D. let graph: Vec = vec![vec![]; n];

Solution

1. Step 1: Identify adjacency list type

   An adjacency list for a graph is usually a vector of vectors, where each inner vector holds neighbors of a vertex.

2. Step 2: Match code snippet to adjacency list

   let graph: Vec = vec![vec![]; n]; declares a vector of vectors of usize, initialized with empty vectors for each vertex, which is correct. Options A, B, and C do not represent adjacency lists properly.

3. Final Answer:

   let graph: Vec = vec![vec![]; n]; -> Option D

4. Quick Check:

   Adjacency list = Vec of Vecs [OK]

Hint: Adjacency list is Vec in Rust [OK]

Common Mistakes:

• Using Vec<usize> instead of Vec
• Using HashMap with wrong types
• Using tuple vector which is edge list, not adjacency list

3. Given the following Rust code snippet to check bipartiteness, what is the output?

let graph = vec![
    vec![1, 3],
    vec![0, 2],
    vec![1, 3],
    vec![0, 2],
];

fn is_bipartite(graph: &Vec) -> bool {
    let n = graph.len();
    let mut colors = vec![-1; n];
    for i in 0..n {
        if colors[i] == -1 {
            let mut stack = vec![i];
            colors[i] = 0;
            while let Some(u) = stack.pop() {
                for &v in &graph[u] {
                    if colors[v] == -1 {
                        colors[v] = 1 - colors[u];
                        stack.push(v);
                    } else if colors[v] == colors[u] {
                        return false;
                    }
                }
            }
        }
    }
    true
}

println!("{}", is_bipartite(&graph));

medium

A. true

B. false

C. Compilation error

D. Runtime panic

Solution

1. Step 1: Analyze graph structure

   The graph has 4 vertices with edges forming a square (0-1-2-3-0). This is an even cycle, which is bipartite.

2. Step 2: Trace DFS coloring

   The code colors vertex 0 as 0, then neighbors 1 and 3 as 1, then neighbors 2 as 0. No conflicts occur, so it returns true.

3. Final Answer:

   true -> Option A

4. Quick Check:

   Even cycle graph is bipartite = true [OK]

Hint: Even cycles are bipartite, so output is true [OK]

Common Mistakes:

• Thinking all cycles are non-bipartite
• Confusing stack usage with queue
• Missing color conflict check

4. The following Rust code attempts to check if a graph is bipartite but has a bug. What is the bug?

fn is_bipartite(graph: &Vec) -> bool {
    let n = graph.len();
    let mut colors = vec![-1; n];
    for i in 0..n {
        if colors[i] == -1 {
            let mut stack = vec![i];
            colors[i] = 0;
            while let Some(u) = stack.pop() {
                for &v in &graph[u] {
                    if colors[v] == -1 {
                        colors[v] = colors[u]; // Bug here
                        stack.push(v);
                    } else if colors[v] == colors[u] {
                        return false;
                    }
                }
            }
        }
    }
    true
}

medium

A. Stack is used instead of queue, causing infinite loop

B. Colors assigned to neighbors are same as current vertex, causing wrong bipartite check

C. colors vector is initialized with zeros instead of -1

D. Graph parameter should be mutable

Solution

1. Step 1: Identify color assignment bug

   The code assigns neighbors the same color as current vertex, which breaks bipartite property requiring opposite colors.

2. Step 2: Understand impact on bipartite check

   This causes false negatives or positives because neighbors must have opposite colors, so the check fails.

3. Final Answer:

   Colors assigned to neighbors are same as current vertex, causing wrong bipartite check -> Option B

4. Quick Check:

   Neighbors must have opposite colors [OK]

Hint: Neighbors must get opposite color, not same [OK]

Common Mistakes:

• Assigning same color to neighbors
• Confusing stack and queue usage
• Initializing colors incorrectly

5. You have a graph with vertices 0 to 5 and edges: (0-1), (1-2), (2-3), (3-0), (3-4), (4-5). Which of the following statements is true about its bipartiteness?

hard

A. The graph is not bipartite because it contains an odd cycle (3-4-5)

B. The graph is not bipartite because it contains an even cycle (0-1-2-3)

C. The graph is bipartite because it contains no odd cycles

D. The graph is bipartite because all vertices have degree 2 or less

Solution

1. Step 1: Identify cycles in the graph

   The graph has a cycle 0-1-2-3-0 which is length 4 (even). The path 3-4-5 is a chain, not a cycle.

2. Step 2: Check bipartite condition

   Graphs with no odd cycles are bipartite. Since the only cycle is even, the graph is bipartite.

3. Final Answer:

   The graph is bipartite because it contains no odd cycles -> Option C

4. Quick Check:

   No odd cycles means bipartite [OK]

Hint: Check for odd cycles; none means bipartite [OK]

Common Mistakes:

• Mistaking chains for cycles
• Thinking even cycles break bipartiteness
• Assuming degree affects bipartiteness