IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v203y2025ics0167947324001543.html
   My bibliography  Save this article

Multiple network embedding for anomaly detection in time series of graphs

Author

Listed:
  • Chen, Guodong
  • Arroyo, Jesús
  • Athreya, Avanti
  • Cape, Joshua
  • Vogelstein, Joshua T.
  • Park, Youngser
  • White, Chris
  • Larson, Jonathan
  • Yang, Weiwei
  • Priebe, Carey E.

Abstract

The problem of anomaly detection in time series of graphs is considered, focusing on two related inference tasks: the detection of anomalous graphs within a time series and the detection of temporally anomalous vertices. These tasks are approached via the adaptation of multiple adjacency spectral embedding (MASE), a statistically principled method for joint graph inference. The effectiveness of the method is demonstrated for these inference tasks, and its performance is assessed based on the nature of detectable anomalies. Theoretical justification is provided, along with insights into its use. The approach identifies anomalous vertices beyond just large degree changes when applied to the Enron communication graph, a large-scale commercial search engine time series, and a larval Drosophila connectome.

Suggested Citation

  • Chen, Guodong & Arroyo, Jesús & Athreya, Avanti & Cape, Joshua & Vogelstein, Joshua T. & Park, Youngser & White, Chris & Larson, Jonathan & Yang, Weiwei & Priebe, Carey E., 2025. "Multiple network embedding for anomaly detection in time series of graphs," Computational Statistics & Data Analysis, Elsevier, vol. 203(C).
    • Handle: RePEc:eee:csdana:v:203:y:2025:i:c:s0167947324001543
    DOI: 10.1016/j.csda.2024.108070
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947324001543
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2024.108070?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Carey E. Priebe & John M. Conroy & David J. Marchette & Youngser Park, 2005. "Scan Statistics on Enron Graphs," Computational and Mathematical Organization Theory, Springer, vol. 11(3), pages 229-247, October.
    2. Daniele Durante & David B. Dunson & Joshua T. Vogelstein, 2017. "Nonparametric Bayes Modeling of Populations of Networks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1516-1530, October.
    3. Catherine Matias & Vincent Miele, 2017. "Statistical clustering of temporal networks through a dynamic stochastic block model," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 1119-1141, September.
    4. P W MacDonald & E Levina & J Zhu, 2022. "Latent space models for multiplex networks with shared structure [Inference for multiple heterogeneous networks with a common invariant subspace]," Biometrika, Biometrika Trust, vol. 109(3), pages 683-706.
    5. Daniele Durante & David B. Dunson & Joshua T. Vogelstein, 2017. "Rejoinder: Nonparametric Bayes Modeling of Populations of Networks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1547-1552, October.
    6. Sandipan Roy & Yves Atchadé & George Michailidis, 2017. "Change point estimation in high dimensional Markov random-field models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 1187-1206, September.
    7. Zhu, Mu & Ghodsi, Ali, 2006. "Automatic dimensionality selection from the scree plot via the use of profile likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 918-930, November.
    8. Minhas, Shahryar & Hoff, Peter D. & Ward, Michael D., 2019. "Inferential Approaches for Network Analysis: AMEN for Latent Factor Models," Political Analysis, Cambridge University Press, vol. 27(2), pages 208-222, April.
    9. Daniel L. Sussman & Minh Tang & Donniell E. Fishkind & Carey E. Priebe, 2012. "A Consistent Adjacency Spectral Embedding for Stochastic Blockmodel Graphs," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 1119-1128, September.
    10. Patrick Rubin‐Delanchy & Joshua Cape & Minh Tang & Carey E. Priebe, 2022. "A statistical interpretation of spectral embedding: The generalised random dot product graph," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1446-1473, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Saxena, Ayushi & Lyzinski, Vince, 2025. "Lost in the shuffle: Testing power in the presence of errorful network vertex labels," Computational Statistics & Data Analysis, Elsevier, vol. 204(C).
    2. Chung, Jaewon & Bridgeford, Eric & Arroyo, Jesus & Pedigo, Benjamin D. & Saad-Eldin, Ali & Gopalakrishnan, Vivek & Xiang, Liang & Priebe, Carey E. & Vogelstein, Joshua T., 2020. "Statistical Connectomics," OSF Preprints ek4n3, Center for Open Science.
    3. repec:osf:osfxxx:ek4n3_v1 is not listed on IDEAS
    4. Vainora, J., 2024. "Latent Position-Based Modeling of Parameter Heterogeneity," Cambridge Working Papers in Economics 2455, Faculty of Economics, University of Cambridge.
    5. Laleh Tafakori & Armin Pourkhanali & Riccardo Rastelli, 2022. "Measuring systemic risk and contagion in the European financial network," Empirical Economics, Springer, vol. 63(1), pages 345-389, July.
    6. Linardi, Fernando & Diks, Cees & van der Leij, Marco & Lazier, Iuri, 2020. "Dynamic interbank network analysis using latent space models," Journal of Economic Dynamics and Control, Elsevier, vol. 112(C).
    7. Yoder, Jordan & Chen, Li & Pao, Henry & Bridgeford, Eric & Levin, Keith & Fishkind, Donniell E. & Priebe, Carey & Lyzinski, Vince, 2020. "Vertex nomination: The canonical sampling and the extended spectral nomination schemes," Computational Statistics & Data Analysis, Elsevier, vol. 145(C).
    8. Silvia D'Angelo & Marco Alfò & Thomas Brendan Murphy, 2020. "Modeling node heterogeneity in latent space models for multidimensional networks," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(3), pages 324-341, August.
    9. Patrick Rubin‐Delanchy & Joshua Cape & Minh Tang & Carey E. Priebe, 2022. "A statistical interpretation of spectral embedding: The generalised random dot product graph," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1446-1473, September.
    10. Ilenia Lovato & Alessia Pini & Aymeric Stamm & Maxime Taquet & Simone Vantini, 2021. "Multiscale null hypothesis testing for network‐valued data: Analysis of brain networks of patients with autism," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(2), pages 372-397, March.
    11. Lovato, Ilenia & Pini, Alessia & Stamm, Aymeric & Vantini, Simone, 2020. "Model-free two-sample test for network-valued data," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    12. Noroozi, Majid & Pensky, Marianna, 2024. "Sparse subspace clustering in diverse multiplex network model," Journal of Multivariate Analysis, Elsevier, vol. 203(C).
    13. Devin Case-Ruchala, 2025. "A paradox of openness: Democracies, financial integration & crisis," The Review of International Organizations, Springer, vol. 20(1), pages 33-58, March.
    14. Emily Kalah Gade & Michael Gabbay & Mohammed M. Hafez & Zane Kelly, 2019. "Networks of Cooperation: Rebel Alliances in Fragmented Civil Wars," Journal of Conflict Resolution, Peace Science Society (International), vol. 63(9), pages 2071-2097, October.
    15. Shin Ji-Hyung & Infante-Rivard Claire & Graham Jinko & McNeney Brad, 2012. "Adjusting for Spurious Gene-by-Environment Interaction Using Case-Parent Triads," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(2), pages 1-23, January.
    16. Yana Melnykov & Marcus Perry, 2024. "On Robust Change Point Detection and Estimation in Multisubject Studies," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 86(2), pages 827-879, August.
    17. Ronaldo F. Zampolo & Frederico H. R. Lopes & Rodrigo M. S. de Oliveira & Martim F. Fernandes & Victor Dmitriev, 2024. "Dimensionality Reduction and Clustering Strategies for Label Propagation in Partial Discharge Data Sets," Energies, MDPI, vol. 17(23), pages 1-18, November.
    18. Jana Diesner & Terrill L. Frantz & Kathleen M. Carley, 2005. "Communication Networks from the Enron Email Corpus “It's Always About the People. Enron is no Different”," Computational and Mathematical Organization Theory, Springer, vol. 11(3), pages 201-228, October.
    19. Jochmans, Koen, 2024. "Nonparametric identification and estimation of stochastic block models from many small networks," Journal of Econometrics, Elsevier, vol. 242(2).
    20. Arno de Caigny & Kristof Coussement & Koen W. de Bock & Stefan Lessmann, 2019. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," Post-Print hal-02275958, HAL.
    21. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W. & Lessmann, Stefan, 2020. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1563-1578.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:203:y:2025:i:c:s0167947324001543. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.