Database Paper Browser

Back to papers

Proximity Graphs for Similarity Search: Fast Construction, Lower Bounds, and Euclidean Separation

Summary: Near-linear algorithm to build proximity graphs for (1+ε)-ANN in doubling metrics with O((1/ε)^λ·n·logΔ) edges and (1/ε)^λ·polylogΔ query time, beating prior Ω(n^2) constructions. Matching lower bounds Ω((1/ε)^λ·n + n·logΔ) on worst-case (non-geometric) inputs and an Euclidean refinement that removes the logΔ factor to O((1/ε)^λ·n) edges while preserving query/construction guarantees. (summarized by gpt-5-mini on Feb 11 2026)

Paper ID
1996
Venue
PODS
Year
2026
Pagerank
4.1945683e-05
Overall Rank
10,007 | 30.39%
DOI
10.1145/3767716

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank Citing Paper Year Venue Pagerank
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 9 of 9 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Previous Page 1 / 1 Next

Semantically Similar Papers