MCI approximates dense nearest neighbor graphs via maximal clique covers and progressive local densification to support fast arbitrary-filtered approximate nearest neighbor search with reduced space.
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4 Pith papers cite this work. Polarity classification is still indexing.
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2026 4verdicts
UNVERDICTED 4representative citing papers
CubeGraph uses hierarchical spatial grids and on-the-fly stitching of per-cell vector graphs to enable single-pass nearest-neighbor search for hybrid vector-spatial queries.
RNSG approximates the range-aware relative neighborhood graph (RRNG) to enable high-performance range-filtered ANN queries with one compact index instead of many.
SkipDisk is a disk-memory hybrid ANN search that achieves 63-85% of HNSW latency at 10-20% memory footprint via dedicated pivots for tighter lower bounds, three-level pruning, and decoupled async I/O.
citing papers explorer
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MCI: A Maximal Clique Index for Efficient Arbitrary-Filtered Approximate Nearest Neighbor Search
MCI approximates dense nearest neighbor graphs via maximal clique covers and progressive local densification to support fast arbitrary-filtered approximate nearest neighbor search with reduced space.
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CubeGraph: Efficient Retrieval-Augmented Generation for Spatial and Temporal Data
CubeGraph uses hierarchical spatial grids and on-the-fly stitching of per-cell vector graphs to enable single-pass nearest-neighbor search for hybrid vector-spatial queries.
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RNSG: A Range-Aware Graph Index for Efficient Range-Filtered Approximate Nearest Neighbor Search
RNSG approximates the range-aware relative neighborhood graph (RRNG) to enable high-performance range-filtered ANN queries with one compact index instead of many.
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Low-Latency Out-of-Core ANN Search in High-Dimensional Space
SkipDisk is a disk-memory hybrid ANN search that achieves 63-85% of HNSW latency at 10-20% memory footprint via dedicated pivots for tighter lower bounds, three-level pruning, and decoupled async I/O.