IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v489y2025ics009630032400626x.html
   My bibliography  Save this article

Change point detection in temporal networks based on graph snapshot similarity measures

Author

Listed:
  • Huang, Xianbin
  • Chen, Liming
  • Chen, Wangyong
  • Hu, Yao

Abstract

This paper addresses the challenge of change point detection in temporal networks, a critical task across various domains, including life sciences and socioeconomic activities. Continuous analysis and problem-solving within dynamic networks are essential in these fields. While much attention has been given to binary cases, this study extends the scope to include change point detection in weighted networks, an important dimension of edge analysis in dynamic networks. We introduce a novel distance metric called the Interval Sum Absolute Difference Distance (ISADD) to measure the distance between two graph snapshots. Additionally, we apply a Gaussian radial basis function to transform this distance into a similarity score between graph snapshots. This similarity score function effectively identifies individual change points. Furthermore, we employ a bisection detection algorithm to extend the method to detect multiple change points. Experimental results on both simulated and real-world data demonstrate the efficacy of the proposed framework.

Suggested Citation

  • Huang, Xianbin & Chen, Liming & Chen, Wangyong & Hu, Yao, 2025. "Change point detection in temporal networks based on graph snapshot similarity measures," Applied Mathematics and Computation, Elsevier, vol. 489(C).
    • Handle: RePEc:eee:apmaco:v:489:y:2025:i:c:s009630032400626x
    DOI: 10.1016/j.amc.2024.129165
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S009630032400626X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2024.129165?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. Ashish Sen & S. Srivastava, 1975. "On tests for detecting change in mean when variance is unknown," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 27(1), pages 479-486, December.
    2. Liu, Shaowen & Caporin, Massimiliano & Paterlini, Sandra, 2021. "Dynamic network analysis of North American financial institutions," Finance Research Letters, Elsevier, vol. 42(C).
    3. Chuangxia Huang & Xian Zhao & Renli Su & Xiaoguang Yang & Xin Yang, 2022. "Dynamic network topology and market performance: A case of the Chinese stock market," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 1962-1978, April.
    4. Li, Huichun & Zhang, Xue & Zhao, Chengli, 2021. "Explaining social events through community evolution on temporal networks," Applied Mathematics and Computation, Elsevier, vol. 404(C).
    5. Yunmeng Lu & Tiezhong Liu & Tiantian Wang, 2021. "Dynamic analysis of emergency inter-organizational communication network under public health emergency: a case study of COVID-19 in Hubei Province of China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 109(3), pages 2003-2026, December.
    6. Al Mugahwi, Mohammed & De La Cruz Cabrera, Omar & Fenu, Caterina & Reichel, Lothar & Rodriguez, Giuseppe, 2021. "Block matrix models for dynamic networks," Applied Mathematics and Computation, Elsevier, vol. 402(C).
    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. Li, Xianghua & Zhen, Xiyuan & Qi, Xin & Han, Huichun & Zhang, Long & Han, Zhen, 2023. "Dynamic community detection based on graph convolutional networks and contrastive learning," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    2. Narayanaswamy Balakrishnan & Laurent Bordes & Christian Paroissin & Jean-Christophe Turlot, 2016. "Single change-point detection methods for small lifetime samples," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 79(5), pages 531-551, July.
    3. Joseph Ngatchou-Wandji & Echarif Elharfaoui & Michel Harel, 2022. "On change-points tests based on two-samples U-Statistics for weakly dependent observations," Statistical Papers, Springer, vol. 63(1), pages 287-316, February.
    4. Venkata Jandhyala & Stergios Fotopoulos & Ian MacNeill & Pengyu Liu, 2013. "Inference for single and multiple change-points in time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(4), pages 423-446, July.
    5. Nguyen, An Pham Ngoc & Mai, Tai Tan & Bezbradica, Marija & Crane, Martin, 2023. "Volatility and returns connectedness in cryptocurrency markets: Insights from graph-based methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    6. Li, Boyan & Diao, Xundi, 2023. "Structural break in different stock index markets in China," The North American Journal of Economics and Finance, Elsevier, vol. 65(C).
    7. Fuqi Chen & Rogemar Mamon & Sévérien Nkurunziza, 2018. "Inference for a change-point problem under a generalised Ornstein–Uhlenbeck setting," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(4), pages 807-853, August.
    8. Sandip Sinharay, 2017. "Some Remarks on Applications of Tests for Detecting A Change Point to Psychometric Problems," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 1149-1161, December.
    9. Minya Xu & Ping-Shou Zhong & Wei Wang, 2016. "Detecting Variance Change-Points for Blocked Time Series and Dependent Panel Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(2), pages 213-226, April.
    10. Jon Vilasuso, 1996. "Changes in the duration of economic expansions and contractions in the United States," Applied Economics Letters, Taylor & Francis Journals, vol. 3(12), pages 803-806.
    11. Philip Preuss & Ruprecht Puchstein & Holger Dette, 2015. "Detection of Multiple Structural Breaks in Multivariate Time Series," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 654-668, June.
    12. Xia Cai & Khamis Khalid Said & Wei Ning, 2016. "Change-point analysis with bathtub shape for the exponential distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(15), pages 2740-2750, November.
    13. Li, Kelong & Xie, Chi & Ouyang, Yingbo & Mo, Tingcheng & Feng, Yusen, 2024. "Tail risk spillovers in the stock and forex markets at the major emergencies: Evidence from the G20 countries," International Review of Financial Analysis, Elsevier, vol. 96(PB).
    14. Gerson N. Cardoso & Geraldo E. Silva, 2024. "Electoral influences on the Brazilian B3 data correlation network," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(1), pages 251-272, January.
    15. Jida Liu & Zheng Fu & Yuwei Song & Ruining Ma & Zebin Zhao, 2024. "How to improve the effectiveness of the cooperation networks of emergency science communication for public health emergencies," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-15, December.
    16. Huang, Chuangxia & Cai, Yaqian & Yang, Xiaoguang & Deng, Yanchen & Yang, Xin, 2023. "Laplacian-energy-like measure: Does it improve the Cross-Sectional Absolute Deviation herding model?," Economic Modelling, Elsevier, vol. 127(C).
    17. Pedro André Cerqueira, 2014. "Business Cycle Synchronization and Volatility Shifts," GEMF Working Papers 2014-19, GEMF, Faculty of Economics, University of Coimbra.
    18. Cheng, Tsung-Lin, 2009. "An efficient algorithm for estimating a change-point," Statistics & Probability Letters, Elsevier, vol. 79(5), pages 559-565, March.
    19. S. G. Li & Y. Q. Zhang & Z. X. Yu & F. Liu, 2021. "Economical user-generated content (UGC) marketing for online stores based on a fine-grained joint model of the consumer purchase decision process," Electronic Commerce Research, Springer, vol. 21(4), pages 1083-1112, December.
    20. A R Brentnall & M J Crowder & D J Hand, 2010. "Likelihood-ratio changepoint features for consumer-behaviour models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(3), pages 462-472, March.

    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:apmaco:v:489:y:2025:i:c:s009630032400626x. 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: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    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.