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Making Graphs Compact by Lossless Contraction

CategoryPublications Publications 313

Authors: Wenfei Fan, Yuanhao Li, Muyang Liu, Can Lu

Name of Conference: ACM Conference on Management of Data (SIGMOD 2021),June 20-25,2021, Xi’an, Shaanxi, China

Date of Publication: June, 2021

Abstract

This paper proposes a scheme to reduce big graphs to small graphs. It contracts obsolete parts, stars, cliques and paths into supernodes. The supernodes carry a synopsis S_Q for each query class Q to abstract key features of the contracted parts for answering queries of Q. The contraction scheme provides a compact graph representation and prioritizes up-to-date data. Better still, it is generic and lossless. We show that the same contracted graph is able to support multiple query classes at the same time, no matter whether their queries are label-based or not, local or non-local. Moreover, existing algorithms for these queries can be readily adapted to compute exact answers by using the synopses when possible, and decontracting the supernodes only when necessary. As a proof of concept, we show how to adapt existing algorithms for subgraph isomorphism, triangle counting and shortest distance to contracted graphs. We also provide an incremental contraction algorithm in response to updates. We experimentally verify that on average, the contraction scheme reduces graphs by 71.2%, and improves the evaluation of these queries by 1.53, 1.42 and 2.14 times, respectively.

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Keywords2021SIGMODWenfei Fan Previous: Next: