Graph-Based Vector Search: An Experimental Evaluation of the State-of-the-Art
Summary: Large-scale survey and evaluation of in-memory graph-based vector search, comparing 12 methods on up to 1B vectors. Five paradigms—seed, incremental insertion, neighborhood propagation, diversification, divide-and-conquer—frame the space; incremental insertion and diversification perform best, base-graph choice hurts scalability, and data-adaptive seeding/diversification is a key future direction. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Ilias Azizi
- 2. Karima Echihabi
- 3. Themis Palpanas
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