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Event Association Analysis Using Graph Rules

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Artificial Neural Networks and Machine Learning – ICANN 2023 (ICANN 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14257))

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Abstract

Event association analysis is used to mine potential association relationships between different events in event data sets, and is widely used in areas such as commodity trading and social media networks. Graph rules can be defined with different forms and expressive capabilities according to relationships in various domains. For the event association domain, we define an event association rule based on statistical knowledge to examine positive and negative associations between events, and the generated patterns fuse other semantic information such as time and location to realize the inference on event data. We innovatively propose a matching method for matching candidate sets, which can further refine the rule matching results to each specific event node so that the results can be logically interpreted. Meanwhile, based on the existing rule matching algorithms, we propose an incremental computation method that can quickly process the incremental part of the event data, effectively saving computational resources and time. We demonstrate the accuracy and efficiency of the algorithm using real-life datasets.

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Authors and Affiliations

  1. Tianjin University, Tianjin, 300350, China

    Pengyue Jiang, Wenjun Wang, Xueli Liu, Lifei Sun & Bowen Dong

Authors
  1. Pengyue Jiang
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  2. Wenjun Wang
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  3. Xueli Liu
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  4. Lifei Sun
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  5. Bowen Dong
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Corresponding author

Correspondence to Xueli Liu .

Editor information

Editors and Affiliations

  1. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. Democritus University of Thrace, Xanthi, Greece

    Antonios Papaleonidas

  3. Lancaster University, Lancaster, UK

    Plamen Angelov

  4. Teesside University, Middlesbrough, UK

    Chrisina Jayne

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Jiang, P., Wang, W., Liu, X., Sun, L., Dong, B. (2023). Event Association Analysis Using Graph Rules. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14257. Springer, Cham. https://doi.org/10.1007/978-3-031-44216-2_29

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  • DOI: https://doi.org/10.1007/978-3-031-44216-2_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-44215-5

  • Online ISBN: 978-3-031-44216-2

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