Cluster-and-Conquer: When Randomness Meets Graph Locality

• URL: http://arxiv.org/abs/2010.11497v1
• Date: Thu, 22 Oct 2020 07:31:12 GMT
• Title: Cluster-and-Conquer: When Randomness Meets Graph Locality
• Authors: George Giakkoupis (WIDE), Anne-Marie Kermarrec (EPFL), Olivier Ruas (SPIRALS), Fran\c{c}ois Ta\"iani (WIDE, IRISA)
• Abstract summary: Some of the most efficient KNN graph algorithms are incremental and local. Cluster-and-Conquer boosts the starting configuration of greedy algorithms.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: K-Nearest-Neighbors (KNN) graphs are central to many emblematic data mining and machine-learning applications. Some of the most efficient KNN graph algorithms are incremental and local: they start from a random graph, which they incrementally improve by traversing neighbors-of-neighbors links. Paradoxically, this random start is also one of the key weaknesses of these algorithms: nodes are initially connected to dissimilar neighbors, that lie far away according to the similarity metric. As a result, incremental algorithms must first laboriously explore spurious potential neighbors before they can identify similar nodes, and start converging. In this paper, we remove this drawback with Cluster-and-Conquer (C 2 for short). Cluster-and-Conquer boosts the starting configuration of greedy algorithms thanks to a novel lightweight clustering mechanism, dubbed FastRandomHash. FastRandomHash leverages random-ness and recursion to pre-cluster similar nodes at a very low cost. Our extensive evaluation on real datasets shows that Cluster-and-Conquer significantly outperforms existing approaches, including LSH, yielding speed-ups of up to x4.42 while incurring only a negligible loss in terms of KNN quality.

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