This is basically a weighted variant of a deduplication or entity-matching problem.
Clarify the problem by defining how field scores are computed (e.g., exact match, similarity) and whether the graph is directed or undirected. Then model merchants as nodes and compute pairwise link scores, building an adjacency list for edges above the threshold. Finally, perform a graph traversal (BFS/DFS) from the target merchant to find all connected merchants.
Pro tip: Discuss scalability early: naive pairwise comparison is O(N^2), so propose blocking or indexing (e.g., by exact match on high-weight fields) to reduce candidate pairs. Also, mention that link scores might be asymmetric if field weights differ, so clarify directionality.
Ask about score computation (exact match vs. fuzzy), directionality of links, and whether the threshold is inclusive. Confirm input/output formats and edge cases (e.g., no links, cycles).
Define a function to compute the link score between two merchants based on matching fields and weights. Build an adjacency list by comparing all merchant pairs (or using an optimized approach) and adding edges where score >= threshold.
Use BFS or DFS starting from the target merchant to explore all reachable nodes. Keep track of visited merchants to avoid cycles and collect the result set.
Discuss time and space complexity: O(N^2) for pairwise comparison and O(N+E) for traversal. Propose optimizations like indexing, blocking, or early termination for large datasets.
Consider cases like no linked merchants, target not in list, duplicate merchants, and threshold variations. Walk through a small example to validate the approach.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.