Recommend two-hop follows in Python
MetaData Engineer · OnsiteLast updated
This question evaluates fluency with graph traversal, set operations, deduplication, and frequency-based ranking in Python, targeting manipulation of directed "follows" relationships represented as dictionaries and is categorized under Data Manipulation (SQL/Python).
Given a directed "follows" graph as a Python dict[str, list[str]], implement recommend_two_hop(graph, user) that returns the set (or a sorted list) of accounts followed by the user’s followees that the user does not already follow, excluding the user themself. Deduplicate recommendations; if you return a list, sort by descending frequency among two-hop neighbors, then lexicographically. Example: graph = {"A": ["B","C"], "B": ["C","D"], "C": ["E"]} ⇒ recommend_two_hop(graph, "A") = {"D","E"}.
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