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Advanced Graph Algorithms Questions

Higher level and combined graph algorithm topics frequently expected at senior or competitive programming levels. Topics include strongly connected components and algorithms such as Kosaraju and Tarjan, minimum spanning trees using Kruskal and Prim with Union Find optimizations, network flow fundamentals including Ford Fulkerson and Edmonds Karp and applications to bipartite matching, graph reductions and transformations, graph coloring and bipartite checks, advanced traversal techniques such as bidirectional search and multi source traversals, and strategies to combine algorithms for complex problems. Emphasize time and space complexity, algorithm correctness proofs, implementation pitfalls, and when to prefer one algorithm or data structure over another.

HardTechnical
0 practiced
Implement Hopcroft–Karp algorithm to compute maximum matching in a bipartite graph. Provide API (language of your choice), explain how layering and BFS/DFS phases produce O(sqrt(V) * E) running time, and include correctness notes and a small test example.
EasyTechnical
0 practiced
Implement a multi-source BFS in Python: given a graph (adjacency list) and a list of source nodes, compute the shortest unweighted distance from each node to its nearest source. State time/space complexity and show sample input/output for a small graph.
MediumTechnical
0 practiced
Describe representations and memory layouts to store very large sparse graphs (millions of nodes, tens/hundreds of millions of edges) for GPU-accelerated GNN training: compare CSR, COO, CSC, and adjacency lists, and explain partitioning strategies (node-wise, edge-wise) and why layout matters for sparse-dense matmul.
HardTechnical
0 practiced
Explain why Edmonds-Karp has polynomial worst-case time complexity and discuss how queue-based augmenting path selection affects performance. Then outline algorithmic improvements (capacity scaling, Dinic, push-relabel) and practical rules to choose among them for production workloads.
MediumTechnical
0 practiced
You're required to support incremental changes to an MST as edges are added and removed in a data-center network topology used for distributed training. Describe algorithms/techniques (e.g., dynamic trees, link-cut trees, rebuild strategies) to maintain MST under updates and discuss practical trade-offs for an AI infrastructure team.

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