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Towards event prediction in temporal graphs

Authors:

Ruiqi XuAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 15, Issue 9

Pages 1861 - 1874

https://doi.org/10.14778/3538598.3538608

Published: 01 May 2022 Publication History

Abstract

This paper proposes a class of temporal association rules, denoted by TACOs, for event prediction. As opposed to previous graph rules, TACOs monitor updates to graphs, and can be used to capture temporal interests in recommendation and catch frauds in response to behavior changes, among other things. TACOs are defined on temporal graphs in terms of change patterns and (temporal) conditions, and may carry machine learning (ML) predicates for temporal event prediction. We settle the complexity of reasoning about TACOs, including their satisfiability, implication and prediction problems. We develop a system, referred to as TASTE. TASTE discovers TACOs by iteratively training a rule creator based on generative ML models in a creator-critic framework. Moreover, it predicts events by applying the discovered TACOs. Using real-life and synthetic datasets, we experimentally verify that TASTE is on average 31.4 times faster than conventional data mining methods in TACO discovery, and it improves the accuracy of state-of-the-art event prediction models by 23.4%.

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Cited By

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 WPang KLu PTian C(2024)Making It Tractable to Detect and Correct Errors in GraphsACM Transactions on Database Systems10.1145/370231549:4(1-75)Online publication date: 2-Nov-2024
https://dl.acm.org/doi/10.1145/3702315
Zhang JYing RShao J(2024)Online Detection of Anomalies in Temporal Knowledge Graphs with InterpretabilityProceedings of the ACM on Management of Data10.1145/36988232:6(1-26)Online publication date: 20-Dec-2024
https://dl.acm.org/doi/10.1145/3698823
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cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 15, Issue 9

May 2022

239 pages

ISSN:2150-8097

Editors:
Fatma Özcan
Google
,
Juliana Freire
New York University
,
Xuemin Lin
University of New South Wales

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Published: 01 May 2022

Published in PVLDB Volume 15, Issue 9

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Cited By

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 WPang KLu PTian C(2024)Making It Tractable to Detect and Correct Errors in GraphsACM Transactions on Database Systems10.1145/370231549:4(1-75)Online publication date: 2-Nov-2024
https://dl.acm.org/doi/10.1145/3702315
Zhang JYing RShao J(2024)Online Detection of Anomalies in Temporal Knowledge Graphs with InterpretabilityProceedings of the ACM on Management of Data10.1145/36988232:6(1-26)Online publication date: 20-Dec-2024
https://dl.acm.org/doi/10.1145/3698823
Zhou YLi X(2024)Event Time Prediction via Survival Analysis Based Multivariate Transformer Point ProcessDatabase Systems for Advanced Applications10.1007/978-981-97-5552-3_2(20-36)Online publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1007/978-981-97-5552-3_2
Fan WTugay RWang YXie MAli M(2023)Learning and Deducing Temporal OrdersProceedings of the VLDB Endowment10.14778/3594512.359452416:8(1944-1957)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.14778/3594512.3594524
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 WFu WJin RLiu MLu PTian C(2023)Making It Tractable to Catch Duplicates and Conflicts in GraphsProceedings of the ACM on Management of Data10.1145/35889401:1(1-28)Online publication date: 30-May-2023
https://dl.acm.org/doi/10.1145/3588940
Fan W(2022)Big graphsProceedings of the VLDB Endowment10.14778/3554821.355489915:12(3782-3797)Online publication date: 29-Sep-2022
https://dl.acm.org/doi/10.14778/3554821.3554899

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