research-article

In-Memory Subgraph Matching: An In-depth Study

Authors:

Qiong LuoAuthors Info & Claims

SIGMOD '20: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data

Pages 1083 - 1098

https://doi.org/10.1145/3318464.3380581

Published: 31 May 2020 Publication History

Abstract

We study the performance of eight representative in-memory subgraph matching algorithms. Specifically, we put QuickSI, GraphQL, CFL, CECI, DP-iso, RI and VF2++ in a common framework to compare them on the following four aspects: (1) method of filtering candidate vertices in the data graph; (2) method of ordering query vertices; (3) method of enumerating partial results; and (4) other optimization techniques. Then, we compare the overall performance of these algorithms with Glasgow, an algorithm based on the constraint programming. Through experiments, we find that (1) the filtering method of GraphQL is competitive to that of the latest algorithms CFL, CECI and DP-iso in terms of pruning power; (2) the ordering methods in GraphQL and RI are usually the most effective; (3) the set intersection based local candidate computation in CECI and DP-iso performs the best in the enumeration; and (4) the failing sets pruning in DP-iso can significantly improve the performance when queries become large. Our source code is publicly available at https://github.com/RapidsAtHKUST/SubgraphMatching.

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Index Terms

In-Memory Subgraph Matching: An In-depth Study
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing

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cover image ACM Conferences

SIGMOD '20: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data

June 2020

2925 pages

ISBN:9781450367356

DOI:10.1145/3318464

General Chairs:
David Maier
Portland State University, USA
,
Rachel Pottinger
University of British Columbia, Canada
,
Program Chairs:
AnHai Doan
University of Wisconsin, USA
,
Wang-Chiew Tan
Megagon Labs, USA
,
Publications Chairs:
Abdussalam Alawini
University of Illinois at Urbana-Champaign, USA
,
Hung Q. Ngo
RelationalAI, USA

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Published: 31 May 2020

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SIGMOD/PODS '20: International Conference on Management of Data

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

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Chen ZZhuang JWang XTang XYang KDu MZhou J(2024)Top-k Graph Similarity Search Algorithm Based on Chi-Square Statistics in Probabilistic GraphsElectronics10.3390/electronics1301019213:1(192)Online publication date: 1-Jan-2024
https://doi.org/10.3390/electronics13010192
Zhou YFang YMa CHou THuang X(2024)Efficient Maximal Motif-Clique Enumeration over Large Heterogeneous Information NetworksProceedings of the VLDB Endowment10.14778/3681954.368197517:11(2946-2959)Online publication date: 30-Aug-2024
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Yang JFang SGu ZMa ZLin XTian Z(2024)TC-Match: Fast Time-Constrained Continuous Subgraph MatchingProceedings of the VLDB Endowment10.14778/3681954.368196317:11(2791-2804)Online publication date: 30-Aug-2024
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Shin WSong SPark KHan W(2024)Cardinality Estimation of Subgraph Matching: A Filtering-Sampling ApproachProceedings of the VLDB Endowment10.14778/3654621.365463517:7(1697-1709)Online publication date: 1-Mar-2024
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Sharma AMehta DWu B(2024)Understanding High-Performance Subgraph Pattern Matching: A Systems PerspectiveProceedings of the 7th Joint Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA)10.1145/3661304.3661897(1-12)Online publication date: 14-Jun-2024
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