research-article

SATMargin: Practical Maximal Frequent Subgraph Mining via Margin Space Sampling

Authors:

Pan LiAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 1495 - 1505

https://doi.org/10.1145/3485447.3512196

Published: 25 April 2022 Publication History

Abstract

Maximal Frequent Subgraph (MFS) mining asks to identify the maximal subgraph that commonly appears in a set of graphs, which has been found valuable in many applications in social science, biology, and other domains. Previous studies focused on reducing the search space of MFSs and discovered the theoretically smallest search space. Despite the success in theory, no practical algorithm can exhaustively search the space as it is huge even for small graphs with only tens of nodes and hundreds of edges. Moreover, deciding whether a subgraph is an MFS needs to solve subgraph monomorphism (SM), an NP-complete problem that introduces extra challenges. Here, we propose a practical MFS mining algorithm that targets large MFSs, named SATMargin. SATMargin adopts random walk in the search space to perform efficient search and utilizes a customized conflict learning Boolean Satisfiability (SAT) algorithm to accelerate SM queries. We design a mechanism that reuses SAT solutions to combine the random walk and the SAT solver effectively. We evaluate SATMargin over synthetic graphs and 6 real-world graph datasets. SATMargin shows superior performance to baselines in finding more and larger MFSs. We further demonstrate the effectiveness of SATMargin in a case study of RNA graphs. The identified frequent subgraph by SATMargin well matches the functional core structure of RNAs previously detected in biological experiments. Our software can be found at https://github.com/MuyiLiu2022/SATMargin-and-Baselines.

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Cited By

Leng FLi FBao YZhang TYu G(2024)FSM-BC-BSP: Frequent Subgraph Mining Algorithm Based on BC-BSPApplied Sciences10.3390/app1408315414:8(3154)Online publication date: 9-Apr-2024
https://doi.org/10.3390/app14083154
Wu YSun RWang XWen DZhang YQin LLin X(2024)Efficient Maximal Frequent Group Enumeration in Temporal Bipartite GraphsProceedings of the VLDB Endowment10.14778/3681954.368199717:11(3243-3255)Online publication date: 30-Aug-2024
https://doi.org/10.14778/3681954.3681997
Wu YSun RWang XZhang YQin LZhang WLin X(2024)Efficient Maximal Temporal Plex Enumeration2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00240(3098-3110)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00240
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Index Terms

SATMargin: Practical Maximal Frequent Subgraph Mining via Margin Space Sampling
1. Information systems
  1. Information systems applications
    1. Data mining
2. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory

Index terms have been assigned to the content through auto-classification.

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cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

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Published: 25 April 2022

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Cited By

Leng FLi FBao YZhang TYu G(2024)FSM-BC-BSP: Frequent Subgraph Mining Algorithm Based on BC-BSPApplied Sciences10.3390/app1408315414:8(3154)Online publication date: 9-Apr-2024
https://doi.org/10.3390/app14083154
Wu YSun RWang XWen DZhang YQin LLin X(2024)Efficient Maximal Frequent Group Enumeration in Temporal Bipartite GraphsProceedings of the VLDB Endowment10.14778/3681954.368199717:11(3243-3255)Online publication date: 30-Aug-2024
https://doi.org/10.14778/3681954.3681997
Wu YSun RWang XZhang YQin LZhang WLin X(2024)Efficient Maximal Temporal Plex Enumeration2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00240(3098-3110)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00240
Sancheti VThomas LPudi V(2024)PMCS: Partition-Based Maximal Frequent Subgraph Mining Using MCS2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC61105.2024.00032(159-168)Online publication date: 2-Jul-2024
https://doi.org/10.1109/COMPSAC61105.2024.00032
Li YChen XGuo WLi XLuo WHuang JZhen HYuan MYan JSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)HardSATGEN: Understanding the Difficulty of Hard SAT Formula Generation and A Strong Structure-Hardness-Aware BaselineProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599837(4414-4425)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599837
Liu WChen DYu MXu Q(2022)A Frequent Subgraph Publishing Algorithm Based on Differential Privacy2022 International Conference on Cloud Computing, Big Data Applications and Software Engineering (CBASE)10.1109/CBASE57816.2022.00032(136-141)Online publication date: Sep-2022
https://doi.org/10.1109/CBASE57816.2022.00032

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