Detecting node propensity changes in the dynamic degree corrected stochastic block model

Lisha YU*, William H. WOODALL, Kwok Leung TSUI

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

28 Citations (Scopus)


Many applications involve dynamic networks for which a sequence of snapshots of network structure is available over time. Studying the evolution of node propensity over time can be important in exploring and analyzing these networks. In this paper, we propose a multivariate surveillance plan to monitor node propensity in the dynamic degree corrected stochastic block model. The method is flexible enough to detect anomalous nodes that arise from different mechanisms, including individual change, individuals switch, and global change. Experiments on simulated and case study social network data streams demonstrate that our surveillance strategy can efficiently detect node propensity changes in dynamic networks.

Original languageEnglish
Pages (from-to)209-227
Number of pages19
JournalSocial Networks
Early online date22 Mar 2018
Publication statusPublished - Jul 2018
Externally publishedYes

Bibliographical note

We would like to thank the anonymous reviewers for their insightful comments and suggestions. The work of W. H. Woodall was supported by NSF grant CMMI-1436365. Kwok-Leung Tsui’s research was supported by the Hong Kong Research Grant Council (Ref. T32-101/15-R), and National Natural Science Foundation of China (Ref. 11471275).


  • Dynamic networks
  • Multivariate control charts
  • Network surveillance
  • Statistical process monitoring


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