I got tripped up building the pairwise similarity matrix first, which burned a good chunk of time.
Clarify the problem by restating the inputs and outputs, then propose a two-phase solution: first build the link graph by computing pairwise similarity scores and applying the threshold, then perform a breadth-first search from the target user up to depth two. Discuss trade-offs between precomputing the full graph versus on-the-fly neighbor generation, and consider scalability for large datasets.
Pro tip: Mention that for large datasets, you can avoid O(n²) pairwise comparisons by using blocking or indexing techniques (e.g., locality-sensitive hashing) to find candidate matches, and emphasize that the two-hop reachability can be computed efficiently with BFS while deduplicating results.
Ask about data size, whether weights are per-field and how they combine, threshold semantics (e.g., >= or >), and whether the graph is directed or undirected. Confirm that 'two hops' means up to two edges away.
Define how to compute a composite similarity score from per-field similarities and weights (e.g., weighted sum or average). Discuss normalization and threshold application to decide if an edge exists.
Choose between precomputing all pairwise similarities (O(n²)) or generating neighbors on demand. For large n, propose indexing/blocking to reduce comparisons, and store the graph as an adjacency list.
Use BFS from the target user, exploring up to depth 2. Collect all visited nodes (excluding the target) and deduplicate if a node is reachable via multiple paths.
Discuss time and space complexity: graph construction O(n²) worst-case, BFS O(V+E). Suggest optimizations like early termination, caching, or distributed processing for scale.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.