There are several including work by Guimera , Newman-Girvan, Factions in UCINET.
Mine (KliqueFinder) is explicitly based on a block model/ERGM framework.
(Snijders & Nowicki also cluster explicitly on a block-model framework).
1) Monte carlo test of statistical significance for the clusters identified
2) Batch mode for multiple applications
3) Two-mode extension
a. *Field, S. *Frank, K.A., Schiller, K, Riegle-Crumb, C, and Muller, C. (2006). "Identifying Social Contexts in Affiliation Networks: Preserving the Duality of People and Events. Social Networks 28:97-123. * coequal first authorship
4) coupled with Netdraw KliqueFinder makes visualizations
The interface is easier than it used to be – see the ppt above.
MSU Foundation Professor of Sociometrics
Measurement and Quantitative Methods
Counseling, Educational Psychology and Special Education
Professor of Fisheries and Wildlife
Room 462 Erickson Hall
620 Farm Lane
Michigan State University
From: Social Networks Discussion Forum [mailto:[log in to unmask]]
On Behalf Of Muhammad Qasim Pasta
Sent: Tuesday, November 08, 2016 7:53 AM
To: [log in to unmask]
Subject: Community detection algorithms for non-overlapping community based on stochastic block models
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Can you refer me any available implementation of community detection algorithms based on stochastic block models for detection of non-overlapping communities?
Thanks in advance,
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