BFS Breadth First Search on Graph in DSA Go - Build from Scratch

BFS Breadth First Search on Graph

📖 Scenario: You are exploring a small network of connected cities. Each city is connected to some other cities by roads. You want to visit all cities starting from a specific city, exploring neighbors first before going deeper.

🎯 Goal: Build a program that uses BFS (Breadth First Search) to visit all cities in the network starting from a given city and print the order of visiting.

📋 What You'll Learn

Create a graph using a map where keys are city names (strings) and values are slices of connected city names.

Create a queue to help with BFS traversal.

Implement BFS using a queue and a visited map to avoid revisiting cities.

Print the order of cities visited by BFS starting from a given city.

💡 Why This Matters

🌍 Real World

BFS is used in social networks to find friends nearby, in maps to find shortest paths, and in many network and web crawling tasks.

💼 Career

Understanding BFS is essential for software engineers working on graph problems, search algorithms, and network analysis.

1

Create the graph data structure

Create a variable called graph of type map[string][]string with these exact entries: "A": {"B", "C"}, "B": {"A", "D", "E"}, "C": {"A", "F"}, "D": {"B"}, "E": {"B", "F"}, "F": {"C", "E"}.

DSA Go

graph := map[string][]string{
    // Your code here
}

Hint

Use a map with string keys and slice of strings as values to represent the graph connections.

2

Create a queue and visited map for BFS

Create a variable called queue as a slice of strings initialized with "A". Create a variable called visited as a map of string to bool, and mark "A" as visited.

DSA Go

package main

import "fmt"

func main() {
    graph := map[string][]string{
        "A": {"B", "C"},
        "B": {"A", "D", "E"},
        "C": {"A", "F"},
        "D": {"B"},
        "E": {"B", "F"},
        "F": {"C", "E"},
    }
    // Your code here
}

Hint

Initialize the queue with the starting city "A" and mark it visited in the map.

3

Implement BFS traversal loop

Use a for loop that runs while len(queue) > 0. Inside the loop, remove the first element from queue into a variable current. For each neighbor in graph[current], if it is not visited, append it to queue and mark it visited.

DSA Go

package main

import "fmt"

func main() {
    graph := map[string][]string{
        "A": {"B", "C"},
        "B": {"A", "D", "E"},
        "C": {"A", "F"},
        "D": {"B"},
        "E": {"B", "F"},
        "F": {"C", "E"},
    }

    queue := []string{"A"}
    visited := map[string]bool{"A": true}

    // Your code here
}

Hint

Use a loop to process the queue until empty. Remove the first city, then add unvisited neighbors to the queue and mark them visited.

4

Print the BFS visiting order

Create a slice of strings called order before the loop. Inside the BFS loop, append current to order. After the loop ends, print order.

DSA Go

package main

import "fmt"

func main() {
    graph := map[string][]string{
        "A": {"B", "C"},
        "B": {"A", "D", "E"},
        "C": {"A", "F"},
        "D": {"B"},
        "E": {"B", "F"},
        "F": {"C", "E"},
    }

    queue := []string{"A"}
    visited := map[string]bool{"A": true}

    // Your code here
}

Hint

Keep track of the visiting order by appending each visited city to a slice, then print it after BFS completes.

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