Number of Islands BFS and DFS in DSA Go - Time & Space Complexity

Time Complexity: Number of Islands BFS and DFS

O(rows x cols)

Understanding Time Complexity

We want to understand how the time needed to find islands grows as the grid size grows.

How does the number of steps change when the grid gets bigger?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

func numIslands(grid [][]byte) int {
    rows := len(grid)
    if rows == 0 {
        return 0
    }
    cols := len(grid[0])
    count := 0
    for r := 0; r < rows; r++ {
        for c := 0; c < cols; c++ {
            if grid[r][c] == '1' {
                bfs(grid, r, c)
                count++
            }
        }
    }
    return count
}

func bfs(grid [][]byte, r, c int) {
    queue := [][]int{{r, c}}
    grid[r][c] = '0'
    directions := [][]int{{1,0}, {-1,0}, {0,1}, {0,-1}}
    for len(queue) > 0 {
        curr := queue[0]
        queue = queue[1:]
        for _, d := range directions {
            nr, nc := curr[0]+d[0], curr[1]+d[1]
            if nr >= 0 && nr < len(grid) && nc >= 0 && nc < len(grid[0]) && grid[nr][nc] == '1' {
                queue = append(queue, []int{nr, nc})
                grid[nr][nc] = '0'
            }
        }
    }
}

This code counts islands in a grid by scanning each cell and using BFS to mark connected land.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Visiting each cell once during the grid scan and BFS traversal.
• How many times: Each cell is checked once in the nested loops, and each land cell is visited once in BFS.

How Execution Grows With Input

As the grid size grows, the number of cells to check grows with rows x columns.

Input Size (n = rows x cols)	Approx. Operations
10	About 10 checks and visits
100	About 100 checks and visits
1000	About 1000 checks and visits

Pattern observation: The steps grow roughly in direct proportion to the total number of cells.

Final Time Complexity

Time Complexity: O(rows x cols)

This means the time needed grows linearly with the total number of cells in the grid.

Common Mistake

[X] Wrong: "The BFS inside the loop makes this O((rows x cols)²) because it repeats for each cell."

[OK] Correct: Each cell is visited only once overall, so BFS does not repeat work on the same cells multiple times.

Interview Connect

Understanding this helps you explain how to efficiently explore connected parts in grids, a common real-world problem.

Self-Check

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

Practice

1. What does the "Number of Islands" problem ask you to find in a grid of '1's and '0's?

easy

A. Count how many groups of connected '1's (islands) are in the grid

B. Find the largest island size

C. Count all '0's in the grid

D. Calculate the perimeter of islands

Solution

1. Step 1: Understand the grid content

   The grid contains '1's representing land and '0's representing water.

2. Step 2: Define the problem goal

   We need to count how many separate groups of connected '1's (islands) exist.

3. Final Answer:

   Count how many groups of connected '1's (islands) are in the grid -> Option A

4. Quick Check:

   Number of islands = count of connected '1' groups [OK]

Hint: Islands = connected '1's groups in grid [OK]

Common Mistakes:

• Counting individual '1's instead of groups
• Confusing '0's as islands
• Trying to find island size instead of count

2. Which of the following Go code snippets correctly starts a DFS from cell (i, j) in a grid to mark connected land?

easy

A. func dfs(grid [][]byte, i, j int) { if grid[i][j] == '1' { grid[i][j] = '1' } }

B. func dfs(grid [][]byte, i, j int) { for i < len(grid) { for j < len(grid[0]) { grid[i][j] = '0' } } }

C. func dfs(grid [][]byte, i, j int) { if i < 0 || j < 0 || i >= len(grid) || j >= len(grid[0]) || grid[i][j] == '0' { return } grid[i][j] = '0'; dfs(grid, i+1, j); dfs(grid, i-1, j); dfs(grid, i, j+1); dfs(grid, i, j-1) }

D. func dfs(grid [][]byte, i, j int) { if i > len(grid) || j > len(grid[0]) { return } grid[i][j] = '1' }

Solution

1. Step 1: Check boundary and water conditions

   func dfs(grid [][]byte, i, j int) { if i < 0 || j < 0 || i >= len(grid) || j >= len(grid[0]) || grid[i][j] == '0' { return } grid[i][j] = '0'; dfs(grid, i+1, j); dfs(grid, i-1, j); dfs(grid, i, j+1); dfs(grid, i, j-1) } correctly checks if indices are out of bounds or cell is water ('0'), then returns.

2. Step 2: Mark current land as visited and recurse

   func dfs(grid [][]byte, i, j int) { if i < 0 || j < 0 || i >= len(grid) || j >= len(grid[0]) || grid[i][j] == '0' { return } grid[i][j] = '0'; dfs(grid, i+1, j); dfs(grid, i-1, j); dfs(grid, i, j+1); dfs(grid, i, j-1) } marks current cell as '0' to avoid revisiting and calls dfs on neighbors up, down, left, right.

3. Final Answer:

   func dfs(grid [][]byte, i, j int) { if i < 0 || j < 0 || i >= len(grid) || j >= len(grid[0]) || grid[i][j] == '0' { return } grid[i][j] = '0'; dfs(grid, i+1, j); dfs(grid, i-1, j); dfs(grid, i, j+1); dfs(grid, i, j-1) } -> Option C

4. Quick Check:

   DFS marks visited land and explores neighbors [OK]

Hint: Check boundaries and mark visited before recursive calls [OK]

Common Mistakes:

• Not checking boundaries before access
• Not marking visited cells
• Using wrong loop instead of recursion

3. What is the output of the following Go code that counts islands using BFS on this grid?

grid := [][]byte{
  {'1','1','0','0','0'},
  {'1','1','0','0','0'},
  {'0','0','1','0','0'},
  {'0','0','0','1','1'},
}
count := numIslands(grid)
fmt.Println(count)

func numIslands(grid [][]byte) int {
  rows, cols := len(grid), len(grid[0])
  count := 0
  directions := [][]int{{1,0},{-1,0},{0,1},{0,-1}}
  for i := 0; i < rows; i++ {
    for j := 0; j < cols; j++ {
      if grid[i][j] == '1' {
        count++
        queue := [][]int{{i,j}}
        grid[i][j] = '0'
        for len(queue) > 0 {
          r, c := queue[0][0], queue[0][1]
          queue = queue[1:]
          for _, d := range directions {
            nr, nc := r+d[0], c+d[1]
            if nr >= 0 && nr < rows && nc >= 0 && nc < cols && grid[nr][nc] == '1' {
              queue = append(queue, []int{nr,nc})
              grid[nr][nc] = '0'
            }
          }
        }
      }
    }
  }
  return count
}

medium

A. 1

B. 3

C. 4

D. 2

Solution

1. Step 1: Identify islands in the grid

   There are three groups of connected '1's: top-left block, middle single '1', bottom-right block.

2. Step 2: BFS marks visited and counts islands

   Each BFS call marks one island, so count increments 3 times.

3. Final Answer:

   3 -> Option B

4. Quick Check:

   Count islands = 3 [OK]

Hint: Count BFS runs triggered by unvisited '1's [OK]

Common Mistakes:

• Counting connected '1's individually
• Not marking visited cells
• Miscounting overlapping islands

4. The following DFS code for counting islands has a bug. What is the main issue?

func dfs(grid [][]byte, i, j int) {
  if i < 0 || j < 0 || i > len(grid) || j > len(grid[0]) || grid[i][j] == '0' {
    return
  }
  grid[i][j] = '0'
  dfs(grid, i+1, j)
  dfs(grid, i-1, j)
  dfs(grid, i, j+1)
  dfs(grid, i, j-1)
}

medium

A. Boundary checks use '>' instead of '>=' causing out-of-range errors

B. It does not mark visited cells

C. It uses BFS instead of DFS

D. It does not check if grid[i][j] is '1'

Solution

1. Step 1: Analyze boundary conditions

   The code uses 'i > len(grid)' and 'j > len(grid[0])' which allows i or j to equal length, causing index out of range.

2. Step 2: Correct boundary check

   It should use 'i >= len(grid)' and 'j >= len(grid[0])' to prevent invalid access.

3. Final Answer:

   Boundary checks use '>' instead of '>=' causing out-of-range errors -> Option A

4. Quick Check:

   Use '>=' for boundary checks to avoid errors [OK]

Hint: Use '>=' for boundary checks in arrays [OK]

Common Mistakes:

• Using '>' instead of '>=' in boundary checks
• Forgetting to mark visited cells
• Mixing BFS and DFS logic

5. Given a grid where diagonal connections also count as connected land, how would you modify the BFS or DFS approach to count islands correctly?

hard

A. Count islands without marking visited cells

B. Only check horizontal neighbors, ignore vertical and diagonal

C. Use BFS only without any direction changes

D. Add diagonal directions (top-left, top-right, bottom-left, bottom-right) to the neighbors list

Solution

1. Step 1: Understand connection rules

   Diagonal connections mean neighbors include 8 directions, not just 4 (up, down, left, right).

2. Step 2: Modify neighbor directions

   Add diagonal directions: (-1,-1), (-1,1), (1,-1), (1,1) to the directions list used in BFS/DFS.

3. Final Answer:

   Add diagonal directions (top-left, top-right, bottom-left, bottom-right) to the neighbors list -> Option D

4. Quick Check:

   Include diagonals in neighbors for diagonal connectivity [OK]

Hint: Add diagonals to directions for 8-way connectivity [OK]

Common Mistakes:

• Ignoring diagonal neighbors
• Not updating directions list
• Counting without marking visited