Graph-Based Retrieval

Graph-based retrieval represents information as nodes and relationships, then uses that structure to find relevant items and connect evidence across multiple steps. Common approaches include knowledge-graph traversal with PageRank or spreading activation, graph and hypergraph retrieval-augmented generation, learned query-conditioned retrievers, and graph indexes such as HNSW for approximate nearest-neighbor search. Systems are tested on multi-hop question answering, document and code search, recommendation, and vector-search benchmarks, with measures of recall, answer quality, latency, and cost. A central tension is whether structural gains in evidence coverage and reasoning justify graph construction and maintenance, while avoiding noisy paths that reduce precision or slow retrieval.