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Hi all,

I also have a method for finding cohesive subgroups which can form
communities called the SCAN method and with its extension the DISSECT
method.

The SCAN method can be found from the New Review of Hypermedia and
Multimedia journal:

*A. Chin* and M. Chignell. “Automatic Detection of Cohesive Subgroups Within
Social Hypertext: A Heuristic Approach”. *New Review of Hypermedia and
Multimedia*. Taylor and Francis, Vol. 14, No. 1, 2008, 121-143.

and the DISSECT method in this forthcoming book chapter:

A. Chin and M. Chignell. "DISSECT: Data-Intensive Socially Similar Evolving
Community Tracker", Book chapter in Computational Social Network Analysis:
Trends, Tools and Research Advances, Computer Communications & Networks
series, Springer-Verlag, London, 2009. [in press]

Thanks.

Alvin

-------------
Alvin Chin
Senior Researcher
Mobile Social Networking Group
Nokia Research Center Beijing
http://research.nokia.com/people/alvin_chin


On Sun, Jan 3, 2010 at 11:25 AM, Ken Frank <[log in to unmask]> wrote:

> *****  To join INSNA, visit http://www.insna.org  *****
>
> I'll throw in my two cents.  KliqueFinder finds non-overlapping cohesive
> subgroups.  It maximizes a criterion related to ERGMs, includes
> significance
> tests, can work on weighted data, ports to netdraw, can work on fairly
> large
> networks.  There is also a version for two-mode data.
>
> It is free:
> https://www.msu.edu/~kenfrank/resources.htm#KliqueFinder
>
> See
>
> Frank. K.A. 1995. Identifying Cohesive Subgroups. Social Networks (17):
> 27-56
>
> Frank, K. 1996. Mapping interactions within and between cohesive subgroups.
> Social Networks 18: 93-119.
>
> And https://www.msu.edu/~kenfrank/research.htm#representation
>
> Ken
>
>
>
>
>
> -----Original Message-----
> From: Social Networks Discussion Forum [mailto:[log in to unmask]] On
> Behalf Of Matthieu Latapy
> Sent: Friday, January 01, 2010 1:53 AM
> To: [log in to unmask]
> Subject: Re: community identification algorithms
>
> *****  To join INSNA, visit http://www.insna.org  *****
>
> Hi all.
>
> As long as I know, and as long as modularity maximisation
> is the objective, the best algorithm currently available
> is the one by Blondel et al.
>
> It is:see
> {see the figure below}. For related articles, see:
>
>
> . extremely simple and elegant,
> . extremely fast even on huge graphs,
> . best in maximising modularity,
> . suitable for weighted networks,
> . able to produce multi-level decomposition,
> . well documented and freely implemented.
>
> See http://sites.google.com/site/findcommunities/ for
> reference and code.
>
> All the best,
> ML
>
> On Thu, Dec 31, 2009 at 06:48:41PM -0500, Steve Eichert wrote:
> > *****  To join INSNA, visit http://www.insna.org  *****
> >
> > Hello,
> >
> > I've been using the Newman Girvan algorithm [1] to identify communities
> > within networks of individuals, however, I've been told by someone who I
> > respect greatly that Newman Girvan isn't the best algorithm to use for
> > identifying communities when dealing with human networks.  So, the
> question
> > I have for the group is: what algorithm would you recommend for
> identifying
> > communities when working with networks of people.
> >
> > [1] http://en.wikipedia.org/wiki/Girvan-Newman_algorithm
> >
> > All the best,
> > Steve
> >
> > _____________________________________________________________________
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>
> --
>
> ---------------
> Matthieu Latapy
> http://www-rp.lip6.fr/~latapy
> http://www.complexnetworks.fr
> -----------------------------
>
> _____________________________________________________________________
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