research-article

Capturing topology in graph pattern matching

Authors:

Tianyu WoAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 5, Issue 4

Pages 310 - 321

https://doi.org/10.14778/2095686.2095690

Published: 01 December 2011 Publication History

Abstract

Graph pattern matching is often defined in terms of subgraph isomorphism, an np-complete problem. To lower its complexity, various extensions of graph simulation have been considered instead. These extensions allow pattern matching to be conducted in cubic-time. However, they fall short of capturing the topology of data graphs, i.e., graphs may have a structure drastically different from pattern graphs they match, and the matches found are often too large to understand and analyze. To rectify these problems, this paper proposes a notion of strong simulation, a revision of graph simulation, for graph pattern matching. (1) We identify a set of criteria for preserving the topology of graphs matched. We show that strong simulation preserves the topology of data graphs and finds a bounded number of matches. (2) We show that strong simulation retains the same complexity as earlier extensions of simulation, by providing a cubic-time algorithm for computing strong simulation. (3) We present the locality property of strong simulation, which allows us to effectively conduct pattern matching on distributed graphs. (4) We experimentally verify the effectiveness and efficiency of these algorithms, using real-life data and synthetic data.

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Cited By

Zhang TCai XChen LYang ZGao YCao BFan J(2024)Towards efficient simulation-based constrained temporal graph pattern matchingWorld Wide Web10.1007/s11280-024-01259-227:3Online publication date: 3-Apr-2024
https://dl.acm.org/doi/10.1007/s11280-024-01259-2
Schramm MBhowmik SRothermel KKalavri VSalihoğlu S(2022)Flexible application-aware approximation for modern distributed graph processing frameworksProceedings of the 5th ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA)10.1145/3534540.3534693(1-10)Online publication date: 12-Jun-2022
https://dl.acm.org/doi/10.1145/3534540.3534693
Wang XZhang QGuo DZhao X(2022)A survey of continuous subgraph matching for dynamic graphsKnowledge and Information Systems10.1007/s10115-022-01753-x65:3(945-989)Online publication date: 19-Oct-2022
https://dl.acm.org/doi/10.1007/s10115-022-01753-x
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Capturing topology in graph pattern matching

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cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 5, Issue 4

December 2011

120 pages

ISSN:2150-8097

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VLDB Endowment

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Published: 01 December 2011

Published in PVLDB Volume 5, Issue 4

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Cited By

Zhang TCai XChen LYang ZGao YCao BFan J(2024)Towards efficient simulation-based constrained temporal graph pattern matchingWorld Wide Web10.1007/s11280-024-01259-227:3Online publication date: 3-Apr-2024
https://dl.acm.org/doi/10.1007/s11280-024-01259-2
Schramm MBhowmik SRothermel KKalavri VSalihoğlu S(2022)Flexible application-aware approximation for modern distributed graph processing frameworksProceedings of the 5th ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA)10.1145/3534540.3534693(1-10)Online publication date: 12-Jun-2022
https://dl.acm.org/doi/10.1145/3534540.3534693
Wang XZhang QGuo DZhao X(2022)A survey of continuous subgraph matching for dynamic graphsKnowledge and Information Systems10.1007/s10115-022-01753-x65:3(945-989)Online publication date: 19-Oct-2022
https://dl.acm.org/doi/10.1007/s10115-022-01753-x
Bouhenni SYahiaoui SNouali-Taboudjemat NKheddouci H(2021)A Survey on Distributed Graph Pattern Matching in Massive GraphsACM Computing Surveys10.1145/343972454:2(1-35)Online publication date: 9-Feb-2021
https://dl.acm.org/doi/10.1145/3439724
Bouhenni SYahiaoui SNouali-Taboudjemat NKheddouci H(2021)Efficient parallel edge-centric approach for relaxed graph pattern matchingThe Journal of Supercomputing10.1007/s11227-021-03938-778:2(1642-1671)Online publication date: 15-Jun-2021
https://dl.acm.org/doi/10.1007/s11227-021-03938-7
Chen XMaier DPottinger RDoan ATan WAlawini ANgo H(2020)Simulation-based Approximate Graph Pattern MatchingProceedings of the 2020 ACM SIGMOD International Conference on Management of Data10.1145/3318464.3384401(2825-2827)Online publication date: 11-Jun-2020
https://dl.acm.org/doi/10.1145/3318464.3384401
Zhang HXie XWen YZhang Y(2020)A Twig-Based Algorithm for Top-k Subgraph Matching in Large-Scale Graph DataWeb Information Systems and Applications10.1007/978-3-030-60029-7_43(475-487)Online publication date: 23-Sep-2020
https://dl.acm.org/doi/10.1007/978-3-030-60029-7_43
Lin PSong QWu YPi J(2019)Discovering Patterns for Fact Checking in Knowledge GraphsJournal of Data and Information Quality10.1145/328648811:3(1-27)Online publication date: 7-May-2019
https://dl.acm.org/doi/10.1145/3286488
Ma YYuan YLiu MWang GWang Y(2019)Graph simulation on large scale temporal graphsGeoinformatica10.1007/s10707-019-00381-y24:1(199-220)Online publication date: 30-Nov-2019
https://dl.acm.org/doi/10.1007/s10707-019-00381-y
Wang XYang LZhu YZhan HJin Y(2019)Querying Knowledge Graphs with Natural LanguagesDatabase and Expert Systems Applications10.1007/978-3-030-27618-8_3(30-46)Online publication date: 26-Aug-2019
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