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Discovering Graph Temporal Association Rules

Authors:

Mohammad Hossein Namaki,

Tingjian GeAuthors Info & Claims

CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

Pages 1697 - 1706

https://doi.org/10.1145/3132847.3133014

Published: 06 November 2017 Publication History

Abstract

Detecting regularities between complex events in temporal graphs is critical for emerging applications. This paper proposes graph temporal association rules (GTAR). A GTAR extends traditional association rules to discover temporal associations for complex events captured by a class of temporal pattern queries. We introduce notions of support and confidence for GTARS and formalize the discovery problem for GTARS. We show that despite the enhanced expressive power, GTARS discovery is feasible over large temporal graphs. We develop an effective rule discovery algorithm, which integrates event mining and rule discovery as a single process, and reduces the redundant computation by leveraging their interaction. Using real-life and synthetic data, we experimentally verify the effectiveness and scalability of the algorithms. Our case study also verifies that GTARS demonstrate highly interpretable associations in real-world networks.

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Cited By

Gionis AOettershagen LSarpe IChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Mining Temporal NetworksCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3641245(1260-1263)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3641245
Ding GYuan ZTang WJiang CJiao Q(2024)Mining Temporal Association Rules for Multivariate Time Series Classification Problems With Both Discrete and Continuous Values Based on Shapelets2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC54092.2024.10831679(115-120)Online publication date: 6-Oct-2024
https://doi.org/10.1109/SMC54092.2024.10831679
Xin HChen L(2024)KartGPS: Knowledge Base Update with Temporal Graph Pattern-based Semantic Rules2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00105(5075-5087)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00105
Show More Cited By

Index Terms

Discovering Graph Temporal Association Rules

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Published In

cover image ACM Conferences

CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

November 2017

2604 pages

ISBN:9781450349185

DOI:10.1145/3132847

General Chairs:
Ee-Peng Lim
Singapore Management University, Singapore
,
Marianne Winslett
University of Illinois at Urbana-Champaign, USA, and Advanced Digital Sciences Center, Singapore
,
Program Chairs:
Mark Sanderson
RMIT, Australia
,
Ada Fu
Chinese University of Hong Kong, Hong Kong
,
Jimeng Sun
Georgia Tech, USA
,
Shane Culpepper
RMIT, Australia
,
Eric Lo
Chinese University of Hong Kong, Hong Kong
,
Joyce Ho
Emory University, USA
,
Debora Donato
Mix Tech, Inc., USA
,
Rakesh Agrawal
Data Insights Laboratories, USA
,
Yu Zheng
Microsoft Research Asia, China
,
Carlos Castillo
Qatar Computing Research Institute, Qatar
,
Aixin Sun
Nanyang Technological University, Singapore
,
Vincent S. Tseng
National Cheng Kung University, Taiwan
,
Chenliang Li
Wuhan University, China

Copyright © 2017 ACM.

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Association for Computing Machinery

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Publication History

Published: 06 November 2017

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Funding Sources

Google Faculty Research Award.
National Science Foundation

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CIKM '17

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CIKM '17: ACM Conference on Information and Knowledge Management

November 6 - 10, 2017

Singapore, Singapore

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CIKM '17 Paper Acceptance Rate 171 of 855 submissions, 20%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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CIKM '25

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The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

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Citations

Cited By

Gionis AOettershagen LSarpe IChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Mining Temporal NetworksCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3641245(1260-1263)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3641245
Ding GYuan ZTang WJiang CJiao Q(2024)Mining Temporal Association Rules for Multivariate Time Series Classification Problems With Both Discrete and Continuous Values Based on Shapelets2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC54092.2024.10831679(115-120)Online publication date: 6-Oct-2024
https://doi.org/10.1109/SMC54092.2024.10831679
Xin HChen L(2024)KartGPS: Knowledge Base Update with Temporal Graph Pattern-based Semantic Rules2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00105(5075-5087)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00105
Liu DKwashie SZhang YZhou GBewong MWu XGuo XHe KFeng Z(2024)An efficient approach for discovering Graph Entity Dependencies (GEDs)Information Systems10.1016/j.is.2024.102421125(102421)Online publication date: Nov-2024
https://doi.org/10.1016/j.is.2024.102421
Gao CZhou ZShi LOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Noether embeddingProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3668213(48185-48202)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3668213
Huang CZhang QGuo DZhao XWang X(2023)Discovering Association Rules with Graph Patterns in Temporal NetworksTsinghua Science and Technology10.26599/TST.2021.901009028:2(344-359)Online publication date: Apr-2023
https://doi.org/10.26599/TST.2021.9010090
Alipourlangouri MMansfield AChiang FWu Y(2023)Inconsistency Detection with Temporal Graph Functional Dependencies2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00042(464-476)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00042
Huynh BNguyen LNguyen DNguyen NNguyen HPham TPham TNguyen LNguyen TVo B(2023)Mining Association Rules from a Single Large GraphCybernetics and Systems10.1080/01969722.2022.216274055:3(693-707)Online publication date: 18-Jan-2023
https://doi.org/10.1080/01969722.2022.2162740
Xue BZou L(2022)Knowledge Graph Quality Management: a Comprehensive SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3150080(1-1)Online publication date: 2022
https://doi.org/10.1109/TKDE.2022.3150080
Song QNamaki MLin PWu Y(2022)Answering Why-Questions for Subgraph QueriesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.304643634:10(4636-4649)Online publication date: 1-Oct-2022
https://doi.org/10.1109/TKDE.2020.3046436
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