***** To join INSNA, visit http://www.insna.org ***** Hi, My method called SCAN and extension DISSECT can be used for identifying communities using cohesive subgroups, centrality and similarity. Chin A, Chignell M (2008) Automatic detection of cohesive subgroups within social hypertext: A heuristic approach. New Review of Hypermedia and Multimedia 14(1):121–143 Chin, A., Chignell, M. DISSECT: Data-Intensive Socially Similar Evolving Community Tracker. In Computational Social Network Analysis: Trends, Tools, and Research Advances (Ed. Ajith Abraham, Aboul-Ella Hassanien, and Vaclav Snasel), London: Springer-Verlag London Limited, 2010, 81-106. Hope that helps. Alvin --------- Alvin Chin Senior Researcher Mobile Social Networking Group Nokia Research Center, Beijing http://research.nokia.com/people/alvin_chin On Wed, Jan 6, 2010 at 4:13 PM, Peter J. Mucha <[log in to unmask]> wrote: > ***** To join INSNA, visit http://www.insna.org ***** > > Thanks to Jim for mentioning our methodology for identifying communities > across multiple times (and/or multiple scales, types of connections). The > reference is > > Mucha, Peter J, Thomas Richardson, Kevin Macon, Mason A. Porter, and > Jukka-Pekka Onnela. 2009. Community Structure in Time-Dependent, Multiscale, > and Multiplex Networks. submitted. http://arxiv.org/abs/0911.1824. > > This is of course not the only way to study communities in dynamic > data---see the reviews previously mentioned in this thread---but it has the > property of being intimately related to the existing modularity > methodologies for community detection, which is IMHO nice for many > circumstances. > > Peter > > -- > Peter J. Mucha, Associate Professor > Department of Mathematics > Carolina Center for Interdisciplinary Applied Mathematics > Institute for Advanced Materials, Nanoscience and Technology > The University of North Carolina at Chapel Hill > Campus Box #3250, UNC, Chapel Hill, NC 27599-3250 > Phone: 919/843-2550, Fax: 919/962-9345, Office: 304B Phillips Hall > [log in to unmask], http://www.unc.edu/~mucha <http://www.unc.edu/%7Emucha> > > > _____________________________________________________________________ > SOCNET is a service of INSNA, the professional association for social > network researchers (http://www.insna.org). To unsubscribe, send > an email message to [log in to unmask] containing the line > UNSUBSCRIBE SOCNET in the body of the message. > _____________________________________________________________________ SOCNET is a service of INSNA, the professional association for social network researchers (http://www.insna.org). To unsubscribe, send an email message to [log in to unmask] containing the line UNSUBSCRIBE SOCNET in the body of the message.