research-article

T-FSM: A Task-Based System for Massively Parallel Frequent Subgraph Pattern Mining from a Big Graph

Authors:

Saugat Adhikari,

Xiaoling WangAuthors Info & Claims

Proceedings of the ACM on Management of Data, Volume 1, Issue 1

Article No.: 74, Pages 1 - 26

https://doi.org/10.1145/3588928

Published: 30 May 2023 Publication History

Abstract

Finding frequent subgraph patterns in a big graph is an important problem with many applications such as classifying chemical compounds and building indexes to speed up graph queries. Since this problem is NP-hard, some recent parallel systems have been developed to accelerate the mining. However, they often have a huge memory cost, very long running time, suboptimal load balancing, and possibly inaccurate results. In this paper, we propose an efficient system called T-FSM for parallel mining of frequent subgraph patterns in a big graph. T-FSM adopts a novel task-based execution engine design to ensure high concurrency, bounded memory consumption, and effective load balancing. It also supports a new anti-monotonic frequentness measure called Fraction-Score, which is more accurate than the widely used MNI measure. Our experiments show that T-FSM is orders of magnitude faster than SOTA systems for frequent subgraph pattern mining. Our system code has been released at https://github.com/lyuheng/T-FSM.

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References

[1]

Ehab Abdelhamid et al. "Scalemine: scalable parallel frequent subgraph mining in a single large graph". In: SC. 2016, pp. 716--727.

[2]

Björn Bringmann and Siegfried Nijssen. "What Is Frequent in a Single Graph?" In: PAKDD. Vol. 5012. Lecture Notes in Computer Science. Springer, 2008, pp. 858--863.

[3]

Harry Kai-Ho Chan et al. "Fraction-Score: A New Support Measure for Co-location Pattern Mining". In: ICDE. IEEE, 2019, pp. 1514--1525.

[4]

Xuhao Chen et al. "Pangolin: An Efficient and Flexible Graph Mining System on CPU and GPU". In: Proc. VLDB Endow. 13.8 (2020), pp. 1190--1205.

Digital Library

[5]

Young-Rae Cho and Aidong Zhang. "Predicting protein function by frequent functional association pattern mining in protein interaction networks". In: IEEE Trans. Inf. Technol. Biomed. 14.1 (2010), pp. 30--36.

Digital Library

[6]

Wei-Ta Chu and Ming-Hung Tsai. "Visual pattern discovery for architecture image classification and product image search". In: International Conference on Multimedia Retrieval, ICMR. ACM, 2012, p. 27.

Digital Library

[7]

Stephen A. Cook. "The Complexity of Theorem-Proving Procedures". In: STOC. ACM, 1971, pp. 151--158.

[8]

Mukund Deshpande et al. "Frequent Substructure-Based Approaches for Classifying Chemical Compounds". In: IEEE Trans. Knowl. Data Eng. 17.8 (2005), pp. 1036--1050.

Digital Library

[9]

DistGraph. https://github.com/zakimjz/DistGraph.

[10]

Mohammed Elseidy et al. "GRAMI: Frequent Subgraph and Pattern Mining in a Single Large Graph". In: Proc. VLDB Endow. 7.7 (2014), pp. 517--528.

Digital Library

[11]

Fractal. https://github.com/dccspeed/fractal.

[12]

GraMi. https://github.com/ehab-abdelhamid/GraMi.

[13]

GSE1730. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE1730.

[14]

Myoungji Han et al. "Efficient Subgraph Matching: Harmonizing Dynamic Programming, Adaptive Matching Order, and Failing Set Together". In: SIGMOD. ACM, 2019, pp. 1429--1446.

[15]

Huahai He and Ambuj K. Singh. "Graphs-at-a-time: query language and access methods for graph databases". In: SIGMOD. ACM, 2008, pp. 405--418.

Digital Library

[16]

Kasra Jamshidi, Rakesh Mahadasa, and Keval Vora. "Peregrine: a pattern-aware graph mining system". In: EuroSys. ACM, 2020, 13:1--13:16.

[17]

Amine Mhedhbi and Semih Salihoglu. "Optimizing Subgraph Queries by Combining Binary and Worst-Case Optimal Joins". In: Proc. VLDB Endow. 12.11 (2019), pp. 1692--1704.

Digital Library

[18]

Siegfried Nijssen and Joost N. Kok. "The Gaston Tool for Frequent Subgraph Mining". In: Electron. Notes Theor. Comput. Sci. 127.1 (2005), pp. 77--87.

Digital Library

[19]

Online Appendix. https://github.com/lyuheng/T-FSM/blob/main/appendix.pdf.

[20]

Pangolin. https://github.com/chenxuhao/GraphMiner.

[21]

Peregrine. https://github.com/pdclab/peregrine.

[22]

Vinicius Vitor dos Santos Dias et al. "Fractal: A General-Purpose Graph Pattern Mining System". In: SIGMOD. ACM, 2019, pp. 1357--1374.

[23]

ScaleMine. https://github.com/ehab-abdelhamid/ScaleMine.

[24]

Madeleine Seeland et al. "Online Structural Graph Clustering Using Frequent Subgraph Mining". In: ECML PKDD. Vol. 6323. Lecture Notes in Computer Science. Springer, 2010, pp. 213--228.

[25]

Shixuan Sun and Qiong Luo. "In-Memory Subgraph Matching: An In-depth Study". In: SIGMOD. 2020, pp. 1083--1098.

[26]

Shixuan Sun et al. "RapidMatch: A Holistic Approach to Subgraph Query Processing". In: Proc. VLDB Endow. 14.2 (2020), pp. 176--188.

Digital Library

[27]

Nilothpal Talukder and Mohammed J. Zaki. "A distributed approach for graph mining in massive networks". In: Data Min. Knowl. Discov. 30.5 (2016), pp. 1024--1052.

Digital Library

[28]

Carlos H. C. Teixeira et al. "Arabesque: a system for distributed graph mining". In: SOSP. ACM, 2015, pp. 425--440.

[29]

Twitter. https://academictorrents.com/details/2399616d26eeb4ae9ac3d05c7fdd98958299efa9.

[30]

Julian R. Ullmann. "An Algorithm for Subgraph Isomorphism". In: J. ACM 23.1 (1976), pp. 31--42.

Digital Library

[31]

Kai Wang et al. "RStream: Marrying Relational Algebra with Streaming for Efficient Graph Mining on a Single Machine". In: OSDI. USENIX Association, 2018, pp. 763--782.

[32]

Lizhi Xiang et al. "cuTS: scaling subgraph isomorphism on distributed multi-GPU systems using trie based data structure". In: SC. ACM, 2021, 69:1--69:14.

[33]

Da Yan et al. "G-thinker: A Distributed Framework for Mining Subgraphs in a Big Graph". In: ICDE. IEEE, 2020, pp. 1369--1380.

[34]

Da Yan et al. "PrefixFPM: A Parallel Framework for General-Purpose Frequent Pattern Mining". In: ICDE. IEEE, 2020, pp. 1938--1941.

[35]

Da Yan et al. "PrefixFPM: a parallel framework for general-purpose mining of frequent and closed patterns". In: VLDB J. 31.2 (2022), pp. 253--286.

Digital Library

[36]

Xifeng Yan and Jiawei Han. "gSpan: Graph-Based Substructure Pattern Mining". In: ICDM. 2002, pp. 721--724.

[37]

Xifeng Yan, Philip S. Yu, and Jiawei Han. "Graph Indexing: A Frequent Structure-based Approach". In: SIGMOD. ACM, 2004, pp. 335--346.

[38]

Zongliang Yue et al. "Biological Network Mining". In: Modeling Transcriptional Regulation. Springer, 2021, pp. 139--151.

[39]

Lei Zou, Lei Chen, and M. Tamer Özsu. "K-Automorphism: A General Framework For Privacy Preserving Network Publication". In: Proc. VLDB Endow. 2.1 (2009), pp. 946--957.

Digital Library

Cited By

Tekin LBostanoglu B(2024)Edge Deletion based Subgraph HidingWSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS10.37394/23209.2024.21.3221(333-347)Online publication date: 17-Jul-2024
https://doi.org/10.37394/23209.2024.21.32
Pan H(2024)Multidimensional clustering analysis of mathematical knowledge difficulty based on GspanJournal of Intelligent & Fuzzy Systems10.3233/JIFS-23427446:4(10045-10058)Online publication date: 18-Apr-2024
https://doi.org/10.3233/JIFS-234274
Khalil JYan DYuan LHan JAdhikari SLong CZhou Y(2024)FSM-Explorer: An Interactive Tool for Frequent Subgraph Pattern Mining From a Big Graph2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00414(5405-5408)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00414
Show More Cited By

Index Terms

T-FSM: A Task-Based System for Massively Parallel Frequent Subgraph Pattern Mining from a Big Graph
1. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory
      1. Graph algorithms
2. Theory of computation
  1. Design and analysis of algorithms
    1. Graph algorithms analysis
    2. Parallel algorithms

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Published In

cover image Proceedings of the ACM on Management of Data

Proceedings of the ACM on Management of Data Volume 1, Issue 1

PACMMOD

May 2023

2807 pages

EISSN:2836-6573

DOI:10.1145/3603164

Editor:
Divyakant Agrawal
UC Santa Barbara, United States

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Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 30 May 2023

Published in PACMMOD Volume 1, Issue 1

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Cited By

Tekin LBostanoglu B(2024)Edge Deletion based Subgraph HidingWSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS10.37394/23209.2024.21.3221(333-347)Online publication date: 17-Jul-2024
https://doi.org/10.37394/23209.2024.21.32
Pan H(2024)Multidimensional clustering analysis of mathematical knowledge difficulty based on GspanJournal of Intelligent & Fuzzy Systems10.3233/JIFS-23427446:4(10045-10058)Online publication date: 18-Apr-2024
https://doi.org/10.3233/JIFS-234274
Khalil JYan DYuan LHan JAdhikari SLong CZhou Y(2024)FSM-Explorer: An Interactive Tool for Frequent Subgraph Pattern Mining From a Big Graph2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00414(5405-5408)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00414
Yuan LAhmad AYan DHan JAdhikari SYu XZhou Y(2024) G 2 -AIMD: A Memory-Efficient Subgraph-Centric Framework for Efficient Subgraph Finding on GPUs 2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00245(3164-3177)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00245
Tian SZeng XHu YWang BLiu YJin YMeng CHong CZhang TWang W(2024)GraphRPM: Risk Pattern Mining on Industrial Large Attributed GraphsMachine Learning and Knowledge Discovery in Databases. Applied Data Science Track10.1007/978-3-031-70381-2_9(133-149)Online publication date: 8-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-70381-2_9

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