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Graph-11: Number of Islands || LeetCode 200 || DFS and BFS Graph Traversal Explained

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🌟 Solve the 'Number of Islands' Problem with DFS and BFS Solutions! 🌟
In this video, we tackle the "Number of Islands" problem, a graph traversal challenge that frequently appears in coding interviews. We'll walk you through both Depth-First Search (DFS) and Breadth-First Search (BFS) approaches to efficiently solve this problem.
What You’ll Learn:
✅ How to traverse 2D grids using DFS and BFS.
✅ Key differences between DFS and BFS in graph traversal.
✅ How to identify connected components in a grid-based graph.
✅ Best practices for solving grid-based problems in coding interviews.
Problem Overview:
Given a 2D grid of '1's (land) and '0's (water), your task is to count the number of distinct islands in the grid. An island is defined as a group of connected '1's, connected horizontally or vertically.
📌 Applications:
1️⃣ Geographical Mapping: Identifying distinct landmasses in geographical surveys or satellite imagery.
2️⃣ Network Connectivity: Detecting isolated clusters or sub-networks in network graphs.
3️⃣ Cluster Analysis: Identifying clusters or groups in datasets for machine learning or data analysis.
Solution Overview:
BFS Approach:
Uses a queue to explore all connected cells in a breadth-first manner.
Marks cells as visited to avoid revisiting.
DFS Approach:
Uses recursion to explore all connected cells in a depth-first manner.
Efficiently handles connected components by traversing each direction recursively.
Time Complexity:
BFS/DFS: O(M x N), where M is the number of rows and N is the number of columns.
Space Complexity:
BFS: O(min(M, N)) for the queue.
DFS: O(M x N) for the recursion stack in the worst case.
👉 Try the Problem Yourself:
Why This Tutorial?
The 'Number of Islands' problem is a great way to practice graph traversal techniques using DFS and BFS. By the end of this tutorial, you'll have a solid understanding of how to solve grid-based graph problems and apply these techniques in coding interviews.
💡 Engage with Us:
Have questions or alternative approaches? Drop your thoughts in the comments! Let’s discuss and learn together.
👉 Don’t forget to LIKE, SUBSCRIBE, and SHARE this video to support the channel and stay updated with more tutorials on algorithms, data structures, and coding tips!
Tags:
#GraphAlgorithm, #NumberOfIslands, #DFSAlgorithm, #BFSAlgorithm, #GridTraversal, #GeographicalMapping, #NetworkConnectivity, #ClusterAnalysis, #CodingInterview, #Algorithms, #ProgrammingTutorial, #TechInterviews, #PythonProgramming, #CodingChallenge, #Optimization
In this video, we tackle the "Number of Islands" problem, a graph traversal challenge that frequently appears in coding interviews. We'll walk you through both Depth-First Search (DFS) and Breadth-First Search (BFS) approaches to efficiently solve this problem.
What You’ll Learn:
✅ How to traverse 2D grids using DFS and BFS.
✅ Key differences between DFS and BFS in graph traversal.
✅ How to identify connected components in a grid-based graph.
✅ Best practices for solving grid-based problems in coding interviews.
Problem Overview:
Given a 2D grid of '1's (land) and '0's (water), your task is to count the number of distinct islands in the grid. An island is defined as a group of connected '1's, connected horizontally or vertically.
📌 Applications:
1️⃣ Geographical Mapping: Identifying distinct landmasses in geographical surveys or satellite imagery.
2️⃣ Network Connectivity: Detecting isolated clusters or sub-networks in network graphs.
3️⃣ Cluster Analysis: Identifying clusters or groups in datasets for machine learning or data analysis.
Solution Overview:
BFS Approach:
Uses a queue to explore all connected cells in a breadth-first manner.
Marks cells as visited to avoid revisiting.
DFS Approach:
Uses recursion to explore all connected cells in a depth-first manner.
Efficiently handles connected components by traversing each direction recursively.
Time Complexity:
BFS/DFS: O(M x N), where M is the number of rows and N is the number of columns.
Space Complexity:
BFS: O(min(M, N)) for the queue.
DFS: O(M x N) for the recursion stack in the worst case.
👉 Try the Problem Yourself:
Why This Tutorial?
The 'Number of Islands' problem is a great way to practice graph traversal techniques using DFS and BFS. By the end of this tutorial, you'll have a solid understanding of how to solve grid-based graph problems and apply these techniques in coding interviews.
💡 Engage with Us:
Have questions or alternative approaches? Drop your thoughts in the comments! Let’s discuss and learn together.
👉 Don’t forget to LIKE, SUBSCRIBE, and SHARE this video to support the channel and stay updated with more tutorials on algorithms, data structures, and coding tips!
Tags:
#GraphAlgorithm, #NumberOfIslands, #DFSAlgorithm, #BFSAlgorithm, #GridTraversal, #GeographicalMapping, #NetworkConnectivity, #ClusterAnalysis, #CodingInterview, #Algorithms, #ProgrammingTutorial, #TechInterviews, #PythonProgramming, #CodingChallenge, #Optimization
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