Overview
Graph traversal and shortest-path algorithms: the atomic ideas distilled from LeetCode study work, plus the deeper study references behind them.
Core Concepts
- DFS and BFS Are Complementary Search Strategies — the fundamental dichotomy
- BFS as Wavefront Propagation — why BFS finds shortest paths in unweighted graphs
- DFS Edge Classification as Structural Revelation — what tree/back/forward/cross edges reveal
- Graph Search Is O(V+E)-Optimal — the complexity floor for full-graph search
- Search Strategies Trade Information Gain Against Entropy — the information-theoretic framing
- Union Find — connectivity without traversal
Study References
Deeper implementation-level material in Atlas/References/:
- Shortest Path Relaxation Operation — the atomic operation shared by Dijkstra, Bellman-Ford, and Floyd-Warshall
- Dijkstra’s Algorithm - The Greedy Invariant and Dijkstra’s Algorithm - Implementation Patterns
- Bellman-Ford Algorithm - Edge Relaxation Pattern and Bellman-Ford Algorithm - Negative Weight Handling
- Floyd-Warshall - All-Pairs Dynamic Programming and Floyd-Warshall - Why Intermediate Nodes Matter
- Kruskal’s Algorithm - Greedy Edge Selection for MST and Kruskal’s Algorithm - Union-Find Integration Pattern
- LeetCode Tree Problem Error Patterns — error-pattern analysis from tree-traversal practice