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2685. Count the Number of Complete Components | Leetcode Daily - Python

☕️ Support the channel! Buy me a boba: https://www.buymeaboba.com This video dives into LeetCode problem 2685, "Count the Number of Complete Components," explaining how to efficiently determine the number of complete connected components in an undirected graph. We start with a clear visual explanation of the problem, defining connected components and what makes them "complete" (every node is connected to every other node within the component). The video then presents two distinct approaches: Depth First Search (DFS) and Disjoint Set Union (Union-Find). The DFS solution is thoroughly explained, including building an adjacency list, traversing the graph, and meticulously checking for completeness by examining all node pairs within each component. We analyze the time and space complexity of the DFS approach, highlighting its strengths and limitations. Next, the video introduces a more optimized solution using the Union-Find data structure, explaining the core concepts of `find` and `union` operations, along with path compression and union by rank optimizations. We walk through the Union-Find code, demonstrating how it efficiently groups nodes into connected components and tracks the number of edges, making the completeness check significantly faster. The time and space complexity of the Union-Find approach is then discussed, emphasizing its near-constant time operations due to the Inverse Ackermann function. The video provides clear code examples in Python for both approaches, making it easy to follow along and implement the solutions. Perfect for coding interview preparation and improving problem-solving skills. Like, Subscribe, and Comment! Let me know what problems you'd like to see solved. Solution Link: https://leetcode.com/problems/count-t... #leetcode #python #algorithms #codinginterview #dailycoding