This is the kind of question where you can tell they want you to keep talking and keep going deeper.
Start by acknowledging that a single-machine BFS won't scale, then propose a distributed BFS using a graph processing framework like Pregel or Giraph. Discuss partitioning strategies, communication overhead, and optimizations like bidirectional search or landmark-based approximations for specific use cases.
Pro tip: Mention that LinkedIn's graph is massive but often queries are for short distances (e.g., 1st, 2nd, 3rd degree connections), so precomputing and caching these common cases can drastically reduce load. Also, consider using a hybrid approach: exact BFS for short distances and approximate methods for longer ones.
Ask about the expected query patterns, latency requirements, and whether exact distances are always needed. This determines if you can use approximations or precomputation.
Propose a vertex-centric model like Pregel where each node processes messages and votes to halt. Discuss partitioning the graph across machines to minimize cross-partition edges.
Use techniques like message combining, asynchronous execution, and compression to reduce network overhead. Consider storing the graph in a distributed key-value store with adjacency lists.
Precompute distances for common queries (e.g., 1st and 2nd degree connections) and cache results. Use a multi-level approach: exact for short distances, approximate for longer.
Compare distributed BFS with bidirectional search, landmark-based methods (e.g., ALT), or graph embeddings. Highlight trade-offs in accuracy, latency, and resource usage.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.