✅ Step 1: Graph Basics + BFS/DFS Foundations
Build intuition around graph traversal and adjacency list representation.
Pacific Atlantic Water Flow — ✅ Step 2: Topological Sorting (Kahn’s Algo + DFS)
Used in DAGs (Directed Acyclic Graphs) — key for scheduling and ordering problems.
Find Eventual Safe States — Minimum Number of Vertices to Reach All Nodes — Sort Items by Groups Respecting Dependencies — ✅ Step 3: Detecting Cycles (Directed + Undirected)
Essential for checking validity in graphs (safe states, deadlocks, etc.)
Detect Cycle in a Directed Graph (Course Schedule II) — Find Eventual Safe States — Reconstruct Itinerary — Find Center of Star Graph — ✅ Step 4: Shortest Path Algorithms (BFS, Dijkstra, Bellman-Ford, 0-1 BFS)
Critical for routing, cost-based traversal.
Shortest Path in Binary Matrix — Path With Minimum Effort — Dijkstra: Network Delay Time — Dijkstra: Cheapest Flights Within K Stops — Bellman Ford: Cheapest Flights Within K Stops — 0-1 BFS: Shortest Path with Alternating Colors — Minimum Cost to Make at Least One Valid Path — ✅ Step 5: Union Find (Disjoint Set Union - DSU)
Powerful for connectivity queries, cycle detection, Kruskal’s MST.
Redundant Connection II — Number of Connected Components in Undirected Graph — Satisfiability of Equality Equations — ✅ Step 6: Minimum Spanning Tree (Prim’s & Kruskal’s)
Find lowest cost to connect all nodes — vital for networking problems.
Minimum Cost to Connect All Points — Connecting Cities With Minimum Cost — Optimize Water Distribution in a Village — Critical Connections in a Network — ✅ Step 7: Backtracking + Graphs Hybrid
Graph traversal + permutations, cycles, etc.
Reconstruct Itinerary — ✅ Step 8: DP on Graphs (Memoized DFS)
Some problems are best solved via topological + memoization (DAGs mostly).
Longest Path in a Directed Acyclic Graph — Longest Increasing Path in a Matrix — All Paths From Source to Target — ✅ Step 9: Advanced Graph Problems (Hard-Level)
Prepare for FAANG and top-tier interviews with these.
Remove Invalid Parentheses —