Start by clarifying requirements and scale, then design a data model that supports versioning and efficient point-in-time queries. Propose a layered architecture: a core graph store with temporal indexing, a query engine for time-travel, and a recommendation service that computes mutual followees with scoring. Discuss trade-offs and optimizations, and outline an implementation plan with key algorithms.
Pro tip: Emphasize that versioning can be achieved by storing edges with validity intervals (valid_from, valid_to) rather than full snapshots, which saves space and simplifies point-in-time queries. Also, mention that recommendation scoring can be done efficiently using a min-heap or approximate algorithms for large-scale graphs.
Ask about expected number of users, average follows per user, query patterns (point-in-time frequency, recommendation latency), and consistency requirements. This informs data structure and algorithm choices.
Propose storing follow edges with timestamps and validity intervals. For each edge (follower, followee), store valid_from and valid_to (null if active). Use an index on (follower, valid_from) and (followee, valid_from) for efficient queries.
For a query at time T, retrieve edges where valid_from <= T and (valid_to > T or valid_to is null). Use binary search on sorted lists or interval trees for efficiency. Discuss how to handle unfollows by updating valid_to.
Compute mutual followees: for a user U, find candidates who are followed by people U follows. Score candidates by number of mutual followees, possibly weighted by recency or other factors. Use efficient set intersections and a priority queue to get top-K.
Mention caching, sharding, and approximate algorithms for scale. Compare interval-based versioning vs. snapshotting. Discuss consistency vs. availability for distributed settings.
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