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Efficient and scalable labeled subgraph matching using SGMatch

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Abstract

Graphs are natural candidates for modeling application domains, such as social networks, pattern recognition, citation networks, or protein–protein interactions. One of the most challenging tasks in managing graphs is subgraph matching over data graphs, which attempts to find one-to-one correspondences, called solutions, among the query and data nodes. To compute solutions, most contemporary techniques use backtracking and recursion. An open research question is whether graphs can be matched based on parts and local solutions can be combined to reach a global matching. In this paper, we present an approach based on graph decomposition called SGMatch to match graphs. We represent graphs in smaller units called graphlets and develop a matching technique to leverage this representation. Pruning strategies use a new notion of edge covering called minimum hub cover and metadata, such as statistics and inverted indices, to reduce the number of matching candidates. Our evaluation of SGMatch versus contemporary algorithms, i.e., VF2, GraphQL, QuickSI, GADDI, or SPath, shows that SGMatch substantially improves the performance of current state-of-the-art techniques for larger query graphs with different structures, i.e., cliques, paths or subgraphs.

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Acknowledgments

This work was completed while Carlos R. Rivero was working as a postdoctoral researcher at the University of Idaho. The ILP formulation discussed in Sect. 3.2 for the computation of minimum hub cover was contributed by Belma Yelbay. We would like to thank the anonymous reviewers and Stanisław P. Radziszowski for their valuable suggestions. This research was supported in part by the National Science Foundation grant DRL 1515550, Idaho National Laboratory grant FFK039, and a University of Idaho Office of Research and Economic Development grant.

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Authors and Affiliations

  1. Department of Computer Science, Rochester Institute of Technology, 102 Lomb Memorial Dr., Rochester, NY, 14623-5608, USA

    Carlos R. Rivero

  2. Department of Computer Science, University of Idaho, JEB Room 236, 875 Perimeter Drive, Moscow, ID, 83844-1010, USA

    Hasan M. Jamil

Authors
  1. Carlos R. Rivero
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  2. Hasan M. Jamil
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Correspondence to Carlos R. Rivero.

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Rivero, C.R., Jamil, H.M. Efficient and scalable labeled subgraph matching using SGMatch. Knowl Inf Syst 51, 61–87 (2017). https://doi.org/10.1007/s10115-016-0968-2

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  • DOI: https://doi.org/10.1007/s10115-016-0968-2

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