Module Review: Graphs

[!NOTE] You have traversed the web, solved the maze, and optimized the grid. Time to consolidate your knowledge.

Key Takeaways

  1. Modeling: Almost any relationship problem can be modeled as a Graph.
  2. Traversal:
    • BFS (Queue) finds shortest paths in unweighted graphs.
    • DFS (Stack/Recursion) explores deep and is key for topological sort.
  3. Shortest Path:
    • Dijkstra (Priority Queue) is standard for weighted graphs.
    • Bellman-Ford handles negative weights.
  4. Connectivity:
    • Union-Find is the fastest way to track connected components dynamically.

Complexity Cheat Sheet

Algorithm Time Complexity Space Complexity Use Case
BFS O(V + E) O(V) Shortest Path (Unweighted)
DFS O(V + E) O(V) Puzzles, Cycle Detection
Dijkstra O(E log V) O(V) Shortest Path (Weighted)
Bellman-Ford O(VE) O(V) Negative Weights
Prim’s O(E log V) O(V) MST (Dense/General)
Kruskal’s O(E log E) O(V) MST (Sparse)
Union-Find O(α(N)) O(N) Dynamic Connectivity
Topo Sort O(V + E) O(V) Dependency Resolution

Interactive: Flashcards

Test your knowledge. Click to flip.

Question

What data structure does BFS use?

Click to flip

Answer

A Queue (FIFO).

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Next Steps

Now that you understand the connections, it’s time to learn how to solve problems efficiently. Next Chapter: Dynamic Programming

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