An Efficient System for Subgraph Discovery
Aparna Shashikant Joshi, Yu Zhang, Petko Bogdanov, Jeong-Hyon Hwang · 2018
Subgraph discovery in a data graph (finding subsets of vertices and edges satisfying a user-specified criteria) is an essential and general graph analytics operation with a wide spectrum of applications. We present Nuri, a general subgraph discovery system that allows users to succinctly specify subgraphs of interest and criteria for ranking them. Given such specifications, Nuri efficiently finds the k most relevant subgraphs. It prioritizes (i.e., expands earlier than others) subgraphs that are more likely to expand into the desired subgraphs (prioritized subgraph expansion) and proactively discards irrelevant subgraphs from which the desired subgraphs cannot be constructed (pruning). Nuri can also efficiently store and retrieve a large number of subgraphs on disk without being limited by the size of main memory. We demonstrate using both real and synthetic datasets that Nuri only on a single core outperforms the closest alternative distributed system running on 40 cores by more than 2 orders of magnitude for clique discovery and 1 order of magnitude for subgraph isomorphism and pattern mining.