Temporal network change detection using network centralities

Yoshitaro Yonamoto, Kai Morino, Kenji Yamanishi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose a novel change detection method for temporal networks. In usual change detection algorithms, change scores are generated from an observed time series. When this change score reaches a threshold, an alert is raised to declare the change. Our method aggregates these change scores and alerts based on network centralities. Many types of changes in a network can be discovered from changes to the network structure. Thus, nodes and links should be monitored in order to recognize changes. However, it is difficult to focus on the appropriate nodes and links when there is little information regarding the dataset. Network centrality such as PageRank measures the importance of nodes in a network based on certain criteria. Therefore, it is natural to apply network centralities in order to improve the accuracy of change detection methods. Our analysis reveals how and when network centrality works well in terms of change detection. Based on this understanding, we propose an aggregating algorithm that emphasizes the appropriate network centralities. Our evaluation of the proposed aggregation algorithm showed highly accurate predictions for an artificial dataset and two real datasets. Our method contributes to extending the field of change detection in temporal networks by utilizing network centralities.

Original languageEnglish
Title of host publicationProceedings - 3rd IEEE International Conference on Data Science and Advanced Analytics, DSAA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages51-60
Number of pages10
ISBN (Electronic)9781509052066
DOIs
Publication statusPublished - Dec 22 2016
Externally publishedYes
Event3rd IEEE International Conference on Data Science and Advanced Analytics, DSAA 2016 - Montreal, Canada
Duration: Oct 17 2016Oct 19 2016

Publication series

NameProceedings - 3rd IEEE International Conference on Data Science and Advanced Analytics, DSAA 2016

Conference

Conference3rd IEEE International Conference on Data Science and Advanced Analytics, DSAA 2016
CountryCanada
CityMontreal
Period10/17/1610/19/16

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All Science Journal Classification (ASJC) codes

  • Information Systems
  • Information Systems and Management
  • Artificial Intelligence

Cite this

Yonamoto, Y., Morino, K., & Yamanishi, K. (2016). Temporal network change detection using network centralities. In Proceedings - 3rd IEEE International Conference on Data Science and Advanced Analytics, DSAA 2016 (pp. 51-60). [7796890] (Proceedings - 3rd IEEE International Conference on Data Science and Advanced Analytics, DSAA 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DSAA.2016.13