Change point detection in social networksCritical review with experiments

Kendrick, L; Musial, K; Gabrys, B

Change point detection in social networksCritical review with experiments

Kendrick, L Musial, K

Gabrys, B

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Publication Type:: Journal Article
Citation:: Computer Science Review, 2018, 29 pp. 1 - 13
Issue Date:: 2018-08-01

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Field	Value	Language
dc.contributor.author	Kendrick, L	en_US
dc.contributor.author	Musial, K https://orcid.org/0000-0001-6038-7647	en_US
dc.contributor.author	Gabrys, B https://orcid.org/0000-0002-0790-2846	en_US
dc.date.available	2020-08-01T19:02:52Z
dc.date.issued	2018-08-01	en_US
dc.identifier.citation	Computer Science Review, 2018, 29 pp. 1 - 13	en_US
dc.identifier.issn	1574-0137	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130199
dc.description.abstract	© 2018 Elsevier Inc. Change point detection in social networks is an important element in developing the understanding of dynamic systems. This complex and growing area of research has no clear guidelines on what methods to use or in which circumstances. This paper critically discusses several possible network metrics to be used for a change point detection problem and conducts an experimental, comparative analysis using the Enron and MIT networks. Bayesian change point detection analysis is conducted on different global graph metrics (Size, Density, Average Clustering Coefficient, Average Shortest Path) as well as metrics derived from the Hierarchical and Block models (Entropy, Edge Probability, No. of Communities, Hierarchy Level Membership). The results produced the posterior probability of a change point at weekly time intervals that were analysed against ground truth change points using precision and recall measures. Results suggest that computationally heavy generative models offer only slightly better results compared to some of the global graph metrics. The simplest metrics used in the experiments, i.e. nodes and links numbers, are the recommended choice for detecting overall structural changes.	en_US
dc.relation.ispartof	Computer Science Review	en_US
dc.relation.isbasedon	10.1016/j.cosrev.2018.05.001	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Computation Theory & Mathematics	en_US
dc.title	Change point detection in social networksCritical review with experiments	en_US
dc.type	Journal Article
utslib.citation.volume	29	en_US
utslib.for	0805 Distributed Computing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	29	en_US

Abstract:

© 2018 Elsevier Inc. Change point detection in social networks is an important element in developing the understanding of dynamic systems. This complex and growing area of research has no clear guidelines on what methods to use or in which circumstances. This paper critically discusses several possible network metrics to be used for a change point detection problem and conducts an experimental, comparative analysis using the Enron and MIT networks. Bayesian change point detection analysis is conducted on different global graph metrics (Size, Density, Average Clustering Coefficient, Average Shortest Path) as well as metrics derived from the Hierarchical and Block models (Entropy, Edge Probability, No. of Communities, Hierarchy Level Membership). The results produced the posterior probability of a change point at weekly time intervals that were analysed against ground truth change points using precision and recall measures. Results suggest that computationally heavy generative models offer only slightly better results compared to some of the global graph metrics. The simplest metrics used in the experiments, i.e. nodes and links numbers, are the recommended choice for detecting overall structural changes.

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http://hdl.handle.net/10453/130199