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

Lisha YU; William H. WOODALL; Kwok Leung TSUI

doi:10.1016/j.socnet.2018.03.004

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 Publications › Journal Article (refereed) › peer-review

31 Citations (Scopus)

Abstract

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 language	English
Pages (from-to)	209-227
Number of pages	19
Journal	Social Networks
Volume	54
Early online date	22 Mar 2018
DOIs	https://doi.org/10.1016/j.socnet.2018.03.004
Publication status	Published - Jul 2018
Externally published	Yes

Funding

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).

Keywords

Dynamic networks
Multivariate control charts
Network surveillance
Statistical process monitoring

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10.1016/j.socnet.2018.03.004

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AU - WOODALL, William H.

AU - TSUI, Kwok Leung

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AB - 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.

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KW - Multivariate control charts

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EP - 227

JO - Social Networks

JF - Social Networks

ER -

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

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Keywords

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