Saldaña, D. Franco, Yu, Yi and Feng, Yang (2017) How many communities are there? Journal of Computational and Graphical Statistics, 26 (1). pp. 171-181. doi:10.1080/10618600.2015.1096790 ISSN 1061-8600.

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Abstract

Stochastic blockmodels and variants thereof are among the most widely used approaches to community detection for social networks and relational data. A stochastic blockmodel partitions the nodes of a network into disjoint sets, called communities. The approach is inherently related to clustering with mixture models; and raises a similar model selection problem for the number of communities. The Bayesian information criterion (BIC) is a popular solution, however, for stochastic blockmodels, the conditional independence assumption given the communities of the endpoints among different edges is usually violated in practice. In this regard, we propose composite likelihood BIC (CL-BIC) to select the number of communities, and we show it is robust against possible misspecifications in the underlying stochastic blockmodel assumptions. We derive the requisite methodology and illustrate the approach using both simulated and real data. Supplementary materials containing the relevant computer code are available online.

Item Type:	Journal Article
Divisions:	Faculty of Science, Engineering and Medicine > Science > Statistics
Journal or Publication Title:	Journal of Computational and Graphical Statistics
Publisher:	American Statistical Association
ISSN:	1061-8600
Official Date:	16 February 2017
Dates:	Date Event 16 February 2017 Published
Volume:	26
Number:	1
Page Range:	pp. 171-181
DOI:	10.1080/10618600.2015.1096790
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access

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