bfs dfs

1. Graph Traversal Algorithms: BFS &
DFS
• Presented by: [Your Name]
• Course: Data Structures
• Instructor: [Instructor’s Name]

2. Introduction
• • Graph traversal means visiting all the
vertices of a graph.
• • BFS and DFS are two primary methods.
• • Common in pathfinding, AI, web crawling.

3. Graph Basics
• • Graph: Vertices (nodes) and Edges
(connections)
• • Types: Directed/Undirected,
Weighted/Unweighted
• • Represented by adjacency list or matrix.

4. What is BFS?
• • Breadth-First Search (BFS) explores
neighbors level by level.
• • Uses a queue (FIFO).
• • Best for shortest paths in unweighted
graphs.

5. What is DFS?
• • Depth-First Search (DFS) goes deep before
backtracking.
• • Uses a stack or recursion.
• • Useful for cycle detection, topological sort.

6. BFS Algorithm Steps
• 1. Choose a start node.
• 2. Mark it visited and enqueue.
• 3. While the queue is not empty:
• - Dequeue a node.
• - Visit all unvisited neighbors, mark and
enqueue them.
• (Pseudo-code shown here)

7. DFS Algorithm Steps
• 1. Choose a start node.
• 2. Mark it visited.
• 3. Visit each unvisited neighbor recursively.
• (Pseudo-code shown here)

8. BFS Example 1
• Graph: A-B-C, A-D, B-E, D-E
• Start: A
• BFS Order: A, B, D, C, E
• Queue at each step: (visuals or table)

9. DFS Example 1
• Same graph as BFS Example 1
• Start: A
• DFS Order: A, B, C, E, D
• Call stack or recursion flow

10. BFS Example 2 – Maze Solver
• • Grid maze
• • BFS explores level-by-level
• • Ensures shortest path is found
• • Visual: Maze/grid animation

11. DFS Example 2 – Puzzle Solver
• • Used in Sudoku, N-Queens, etc.
• • Explores deeply
• • Backtracks on failure

12. BFS vs DFS Comparison
• Feature BFS DFS
• Data Struct. Queue Stack/Recursion
• Path Found Shortest (yes) Not guaranteed
• Space Usage High (wide graphs)Low
• Application Pathfinding Cycle detection

13. BFS Applications
• • Finding shortest paths
• • Social networks (mutual friends)
• • GPS navigation
• • Web crawlers

14. DFS Applications
• • Cycle detection
• • Topological sorting
• • Solving puzzles & games
• • Checking connectivity

15. BFS Example 3 – Social Network
• • Nodes = People, Edges = Friendships
• • BFS helps find:
• - Friends of friends
• - Social distances

16. DFS Example 3 – File System
• • Folders = nodes, subfolders = edges
• • DFS explores folder hierarchy
• • Used in deep file search

17. Conclusion & Q/A
• • BFS and DFS are fundamental algorithms
• • Know when to use each
• • Any questions?

18. Real-World Analogies
• • BFS: Like waves spreading out in a pond –
reaches nearby nodes first.
• • DFS: Like exploring a cave – go deep,
backtrack on dead ends.

19. Mini Use Case – BFS: Emergency
Evacuation
• In an office building, BFS helps find the
shortest route to the nearest exit.
• Simulates real-time emergency escape paths.

20. Mini Use Case – DFS: Exploring a
Maze
• DFS is like someone trying every possible path
in a maze.
• Goes deep into one corridor until a dead end,
then backtracks.

21. Advanced Variants
• • Bidirectional BFS – Efficient for huge graphs.
• • Iterative Deepening DFS – Combines DFS
space with BFS completeness.
• • BFS with Priority Queue – Leads into
Dijkstra’s Algorithm.

22. Code Walkthrough – BFS (C++)
• // BFS Code Sample in C++
• queue<int> q;
• visited[start] = true;
• q.push(start);
• while (!q.empty()) {
• int node = q.front(); q.pop();
• for (int neighbor : adj[node]) {
• if (!visited[neighbor]) {
• visited[neighbor] = true;

23. Memory Usage Comparison
• • Wide Graphs: BFS uses more memory
(queue grows large).
• • Deep Graphs: DFS is space-efficient (stack or
recursion depth).

24. BFS & DFS in Interviews
• • Detect cycles in directed graph using DFS.
• • Use BFS when shortest path is needed in
unweighted graph.
• • When to use DFS vs BFS?

25. BFS & DFS in Trees vs Graphs
• • In trees: No cycles – simple traversal.
• • In graphs: Must track visited to avoid infinite
loops.
• • More complex in cyclic/undirected graphs.

26. Bonus Example – Web Crawler
• • BFS starts from a URL, visits all directly
linked pages.
• • Great for level-based crawling like site map
generation.

27. Summary Diagram / Mind Map
• BFS: Queue → Level Order → Shortest Path
• DFS: Stack/Recursion → Depth → Cycle
Detection
• Use cases, code, and analogies combined
visually.