research-article

Association rules with graph patterns

Editors: Chen Li, Volker Markl Authors:

Jingbo XuAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 8, Issue 12

Pages 1502 - 1513

https://doi.org/10.14778/2824032.2824048

Published: 01 August 2015 Publication History

Abstract

We propose graph-pattern association rules (GPARs) for social media marketing. Extending association rules for item-sets, GPARs help us discover regularities between entities in social graphs, and identify potential customers by exploring social influence. We study the problem of discovering top-k diversified GPARs. While this problem is NP-hard, we develop a parallel algorithm with accuracy bound. We also study the problem of identifying potential customers with GPARs. While it is also NP-hard, we provide a parallel scalable algorithm that guarantees a polynomial speedup over sequential algorithms with the increase of processors. Using real-life and synthetic graphs, we experimentally verify the scalability and effectiveness of the algorithms.

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Cited By

Liu XDong BFu WWu NWang XWang W(2024)Extending Graph Rules with OraclesProceedings of the VLDB Endowment10.14778/3654621.365464117:7(1775-1787)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.14778/3654621.3654641
Fan WLiu MLiu STian C(2024)Capturing More Associations by Referencing External GraphsProceedings of the VLDB Endowment10.14778/3648160.364816217:6(1173-1186)Online publication date: 3-May-2024
https://doi.org/10.14778/3648160.3648162
Fan WHan ZXie MZhang G(2024)Discovering Top-k Relevant and Diversified RulesProceedings of the ACM on Management of Data10.1145/36771312:4(1-28)Online publication date: 30-Sep-2024
https://dl.acm.org/doi/10.1145/3677131
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Association rules with graph patterns
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cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 8, Issue 12

Proceedings of the 41st International Conference on Very Large Data Bases, Kohala Coast, Hawaii

August 2015

728 pages

ISSN:2150-8097

Editors:
Chen Li
University of California, Irvine
,
Volker Markl
TU Berlin

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VLDB Endowment

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Published: 01 August 2015

Published in PVLDB Volume 8, Issue 12

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Cited By

Liu XDong BFu WWu NWang XWang W(2024)Extending Graph Rules with OraclesProceedings of the VLDB Endowment10.14778/3654621.365464117:7(1775-1787)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.14778/3654621.3654641
Fan WLiu MLiu STian C(2024)Capturing More Associations by Referencing External GraphsProceedings of the VLDB Endowment10.14778/3648160.364816217:6(1173-1186)Online publication date: 3-May-2024
https://doi.org/10.14778/3648160.3648162
Fan WHan ZXie MZhang G(2024)Discovering Top-k Relevant and Diversified RulesProceedings of the ACM on Management of Data10.1145/36771312:4(1-28)Online publication date: 30-Sep-2024
https://dl.acm.org/doi/10.1145/3677131
Sasaki YKarras PSerra ESpezzano F(2024)Mining Path Association Rules in Large Property GraphsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679525(1994-2003)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679525
Shimomura LYakovets NFletcher G(2024)Reasoning on property graphs with graph generating dependenciesInformation Sciences: an International Journal10.1016/j.ins.2024.120675672:COnline publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1016/j.ins.2024.120675
Manouvrier MBelhajjame K(2024)PG-FD: Mapping Functional Dependencies to the Future Property Graph Schema StandardAdvances in Databases and Information Systems10.1007/978-3-031-70626-4_4(45-59)Online publication date: 28-Aug-2024
https://dl.acm.org/doi/10.1007/978-3-031-70626-4_4
Fan LFan WLu PTian CYin Q(2023)Enriching Recommendation Models with Logic ConditionsProceedings of the ACM on Management of Data10.1145/36173301:3(1-28)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3617330
Fan WHan ZWang YXie M(2023)Discovering Top-k Rules using Subjective and Objective CriteriaProceedings of the ACM on Management of Data10.1145/35889241:1(1-29)Online publication date: 30-May-2023
https://dl.acm.org/doi/10.1145/3588924
Preti GDe Francisci Morales GRiondato M(2023)MaNIACS: Approximate Mining of Frequent Subgraph Patterns through SamplingACM Transactions on Intelligent Systems and Technology10.1145/358725414:3(1-29)Online publication date: 13-Apr-2023
https://dl.acm.org/doi/10.1145/3587254
Jiang PWang WLiu XSun LDong B(2023)Event Association Analysis Using Graph RulesArtificial Neural Networks and Machine Learning – ICANN 202310.1007/978-3-031-44216-2_29(352-363)Online publication date: 26-Sep-2023
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