Graphs and Graph Algorithms Questions

Graph representations (adjacency list and adjacency matrix) and the traversal algorithms applied to general, non-tree structures: BFS, DFS, topological sort (Kahn's algorithm and DFS-based), shortest paths (Dijkstra, Bellman-Ford, A*), minimum spanning trees, cycle detection, connected components, and union-find. Covers modeling a problem as a graph even when the underlying data is not obviously graph-shaped, such as state-space search, an implicit graph over strings or grid cells (for example Word Ladder), or a task-dependency graph, and implementing these traversals with a hash map or hash set as the storage vehicle (adjacency map, visited set, memoization table), not the subject being tested. The graded skill is traversal, ordering, connectivity, or shortest-path reasoning over nodes and edges. This topic does not own: traversal, reconstruction, or serialization of a single-rooted binary tree (preorder, inorder, postorder, or level-order implementation, rebuilding a tree from traversal arrays, lowest common ancestor, binary search tree validation), which belongs to binary trees and binary search trees even though a tree is technically a graph; hash table internals such as hash function design, collision resolution, and load factor and resizing, which belong to hashing and hash tables; and deriving or comparing algorithmic complexity across graph algorithms without implementing them, such as comparing the time complexity of BFS, DFS, Dijkstra, and A*, which belongs to time and space complexity analysis. One of the highest-signal areas in senior coding interviews.