BFS Breadth First Search on Graph in DSA Go - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to initialize the queue with the start node.

DSA Go

queue := []int[1]
visited := make(map[int]bool)
visited[start] = true

Drag options to blanks, or click blank then click option'

Astart

B0

Cnil

D[]int{}

Attempts:

3 left

💡 Hint

Common Mistakes

Initializing queue as empty instead of with start node

Using zero instead of start node

The BFS queue starts with the start node to begin traversal.

2fill in blank

medium

Complete the code to dequeue the first element from the queue.

DSA Go

current := queue[[1]]
queue = queue[1:]

Drag options to blanks, or click blank then click option'

A-1

B1

C0

Dlen(queue)

Attempts:

3 left

💡 Hint

Common Mistakes

Using index 1 which skips the first element

Using negative index which is invalid

In Go slices, the first element is at index 0, so we dequeue from index 0.

3fill in blank

hard

Fix the error in checking if a neighbor node has been visited.

DSA Go

if !visited[[1]] {
    queue = append(queue, neighbor)
    visited[neighbor] = true
}

Drag options to blanks, or click blank then click option'

Acurrent

Bneighbor

Cstart

Dqueue

Attempts:

3 left

💡 Hint

Common Mistakes

Checking visited for current node instead of neighbor

Using wrong variable names

We check if the neighbor node has been visited before adding it to the queue.

4fill in blank

hard

Fill both blanks to correctly loop over neighbors and mark them visited.

DSA Go

for _, [1] := range graph[current] {
    if !visited[[2]] {
        queue = append(queue, neighbor)
        visited[neighbor] = true
    }
}

Drag options to blanks, or click blank then click option'

Aneighbor

Bnode

Dcurrent

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names causing confusion

Checking visited for wrong variable

The loop variable should be 'neighbor' and the visited check should use 'neighbor'.

5fill in blank

hard

Fill all three blanks to build the BFS function that returns the order of visited nodes.

DSA Go

func BFS(graph map[int][]int, start int) []int {
    var order []int
    queue := []int[1]
    visited := make(map[int]bool)
    visited[start] = true

    for len(queue) > 0 {
        current := queue[[2]]
        queue = queue[1:]
        order = append(order, current)

        for _, [3] := range graph[current] {
            if !visited[neighbor] {
                queue = append(queue, neighbor)
                visited[neighbor] = true
            }
        }
    }
    return order
}

Drag options to blanks, or click blank then click option'

Astart

B0

Cneighbor

Dcurrent

Attempts:

3 left

💡 Hint

Common Mistakes

Wrong queue initialization

Incorrect dequeue index

Inconsistent loop variable names

The BFS starts with the start node in the queue, dequeues from index 0, and loops over neighbors using 'neighbor'.

Practice

1. What is the main purpose of BFS (Breadth First Search) on a graph?

easy

A. To sort nodes in descending order

B. To find the shortest path using depth-first traversal

C. To explore all nodes level by level starting from a source node

D. To find cycles in a graph using recursion

Solution

1. Step 1: Understand BFS traversal

   BFS explores nodes by visiting all neighbors of a node before moving to the next level.

2. Step 2: Compare BFS with other traversals

   Unlike DFS, BFS uses a queue to explore nodes level by level, which helps in shortest path problems.

3. Final Answer:

   To explore all nodes level by level starting from a source node -> Option C

4. Quick Check:

   BFS = Level-wise exploration [OK]

Hint: BFS uses a queue to explore nodes level by level [OK]

Common Mistakes:

• Confusing BFS with DFS
• Thinking BFS uses recursion
• Assuming BFS sorts nodes

2. Which of the following Go code snippets correctly initializes a queue for BFS traversal?

easy

A. queue := []int{} // empty slice as queue

B. queue := make(map[int]bool) // map as queue

C. queue := 0 // integer as queue

D. queue := make(chan int) // channel as queue

Solution

1. Step 1: Identify data structure for BFS queue

   BFS requires a FIFO structure; a slice can be used as a queue in Go.

2. Step 2: Check each option

   queue := []int{} // empty slice as queue uses a slice which can enqueue and dequeue elements. queue := make(map[int]bool) // map as queue is a map, not suitable as queue. queue := 0 // integer as queue is an int, not a queue. queue := make(chan int) // channel as queue is a channel, which can be used but requires goroutines and is complex.

3. Final Answer:

   queue := []int{} // empty slice as queue -> Option A

4. Quick Check:

   Slice as queue = queue := []int{} // empty slice as queue [OK]

Hint: Use slice for queue in BFS, not map or int [OK]

Common Mistakes:

• Using map instead of slice for queue
• Using int variable as queue
• Confusing channels with simple queue

3. What is the output of the following Go BFS traversal code on the graph below?

Graph edges:
0 -> 1, 2
1 -> 2
2 -> 0, 3
3 -> 3

package main
import "fmt"
func bfs(graph map[int][]int, start int) []int {
    visited := make(map[int]bool)
    queue := []int{start}
    order := []int{}
    visited[start] = true
    for len(queue) > 0 {
        node := queue[0]
        queue = queue[1:]
        order = append(order, node)
        for _, neighbor := range graph[node] {
            if !visited[neighbor] {
                visited[neighbor] = true
                queue = append(queue, neighbor)
            }
        }
    }
    return order
}
func main() {
    graph := map[int][]int{
        0: {1, 2},
        1: {2},
        2: {0, 3},
        3: {3},
    }
    fmt.Println(bfs(graph, 2))
}

medium

A. [2, 0, 1, 3]

B. [2, 0, 3, 1]

C. [2, 3, 0, 1]

D. [2, 1, 0, 3]

Solution

1. Step 1: Start BFS from node 2

   Initialize queue=[2], visited={2}. Dequeue 2, enqueue neighbors 0 and 3. order=[2]

2. Step 2: Process nodes in queue

   Queue=[0,3]. Dequeue 0, enqueue neighbor 1 (2 already visited). order=[2,0]. Queue=[3,1]. Dequeue 3, neighbor 3 already visited. order=[2,0,3]. Queue=[1]. Dequeue 1, neighbor 2 visited. order=[2,0,3,1]. Queue empty.

3. Final Answer:

   [2, 0, 3, 1] -> Option B

4. Quick Check:

   BFS order from 2 = [2,0,3,1] [OK]

Hint: Track queue and visited sets stepwise [OK]

Common Mistakes:

• Not marking visited before enqueue
• Wrong order of neighbors enqueued
• Confusing BFS with DFS order

4. Identify the bug in the following BFS Go code snippet and select the correct fix.

func bfs(graph map[int][]int, start int) []int {
    visited := make(map[int]bool)
    queue := []int{start}
    order := []int{}
    for len(queue) > 0 {
        node := queue[0]
        queue = queue[1:]
        order = append(order, node)
        for _, neighbor := range graph[node] {
            if !visited[neighbor] {
                queue = append(queue, neighbor)
                visited[neighbor] = true
            }
        }
    }
    return order
}

medium

A. Mark start node as visited before loop

B. Initialize order slice with capacity

C. Use a stack instead of queue

D. Remove visited map entirely

Solution

1. Step 1: Check visited marking for start node

   The code never marks the start node as visited before starting, so it may revisit it.

2. Step 2: Fix by marking start visited before loop

   Adding visited[start] = true before the loop prevents revisiting the start node and infinite loops.

3. Final Answer:

   Mark start node as visited before loop -> Option A

4. Quick Check:

   Start visited before loop = Mark start node as visited before loop [OK]

Hint: Always mark start visited before BFS loop [OK]

Common Mistakes:

• Marking visited after enqueue
• Using stack instead of queue
• Ignoring visited map

5. Given a directed graph represented as adjacency list, how can BFS be adapted to find the shortest path length from a start node to a target node? Choose the best approach.

hard

A. Use BFS but enqueue nodes multiple times to find shortest path

B. Use DFS and backtrack to find all paths, then pick shortest

C. Use BFS but ignore visited nodes to explore all paths

D. Use BFS with a distance map to track levels from start, stop when target found

Solution

1. Step 1: Understand BFS for shortest path

   BFS explores nodes level by level, so the first time target is reached is the shortest path.

2. Step 2: Use a distance map to track levels

   Maintain a map to record distance from start for each node. When target is dequeued, return its distance.

3. Final Answer:

   Use BFS with a distance map to track levels from start, stop when target found -> Option D

4. Quick Check:

   BFS + distance map = shortest path [OK]

Hint: Track distance in BFS, stop at target [OK]

Common Mistakes:

• Using DFS for shortest path in unweighted graph
• Ignoring visited nodes causing infinite loops
• Enqueuing nodes multiple times unnecessarily