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Graph similarity learning for change-point detection in dynamic networks

Author

Listed:
  • Deborah Sulem
  • Henry Kenlay
  • Mihai Cucuringu
  • Xiaowen Dong

Abstract

Dynamic networks are ubiquitous for modelling sequential graph-structured data, e.g., brain connectome, population flows and messages exchanges. In this work, we consider dynamic networks that are temporal sequences of graph snapshots, and aim at detecting abrupt changes in their structure. This task is often termed network change-point detection and has numerous applications, such as fraud detection or physical motion monitoring. Leveraging a graph neural network model, we design a method to perform online network change-point detection that can adapt to the specific network domain and localise changes with no delay. The main novelty of our method is to use a siamese graph neural network architecture for learning a data-driven graph similarity function, which allows to effectively compare the current graph and its recent history. Importantly, our method does not require prior knowledge on the network generative distribution and is agnostic to the type of change-points; moreover, it can be applied to a large variety of networks, that include for instance edge weights and node attributes. We show on synthetic and real data that our method enjoys a number of benefits: it is able to learn an adequate graph similarity function for performing online network change-point detection in diverse types of change-point settings, and requires a shorter data history to detect changes than most existing state-of-the-art baselines.

Suggested Citation

  • Deborah Sulem & Henry Kenlay & Mihai Cucuringu & Xiaowen Dong, 2022. "Graph similarity learning for change-point detection in dynamic networks," Papers 2203.15470, arXiv.org.
  • Handle: RePEc:arx:papers:2203.15470
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    References listed on IDEAS

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    1. Stefanos Bennett & Mihai Cucuringu & Gesine Reinert, 2022. "Lead-lag detection and network clustering for multivariate time series with an application to the US equity market," Papers 2201.08283, arXiv.org.
    2. Areejit Samal & Hirdesh K. Pharasi & Sarath Jyotsna Ramaia & Harish Kannan & Emil Saucan & Jurgen Jost & Anirban Chakraborti, 2020. "Network geometry and market instability," Papers 2009.12335, arXiv.org, revised Jan 2021.
    3. Ivor Cribben & Yi Yu, 2017. "Estimating whole-brain dynamics by using spectral clustering," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(3), pages 607-627, April.
    4. Anirban Chakraborti & Hrishidev & Kiran Sharma & Hirdesh K. Pharasi, 2019. "Phase separation and scaling in correlation structures of financial markets," Papers 1910.06242, arXiv.org, revised Jul 2020.
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