***** To join INSNA, visit http://www.insna.org ***** Hi - I'd argue that stability is distinct from structural cohesion -- the substantive point is to be able to ask theoretically whether membership in a particular network structure leads to stability in relations/membership (particularly beyond dyadic features) rather than building stability into the measure/meaning of the tie. One of the strong advantages of a network approach to cohesion over the LOOOONNNNNG and confused literature in sociology on "group cohesion," is that we don't build the outcome (feeling good, performance, etc. etc.) into the definition of cohesion, but instead treat it as an outcome for testing. The node-cohesion metric will remain stable if the group stays the same over time. If all members of the group maintain their same tie pattern from t1 to t2 -- as suggested in your example -- then their k-connected set will be the same regardless of what happens elsewhere in the graph. This is the advantage of a hard-coded model rather than a relative-density model. (there are obvious tradeoffs of course -- fluctuations due to edge-level measurement error being a strong one). If cohesion tracks the difficult in separating pairs (or equivalently the number of unique routes for transmission); this is exactly what you want. If actors add ties outside their group, then the k-connected set is still stable as a minimum kernel of membership. That is, others may be added to the group if they add ties to new nodes, but without dropping ties inside the group, the k-connectivity remains the same. If you do what to respect time directly, there are ways to do so. One approach our group is working on is to treat time *as* structure by building "identity edges" linking nodes to themselves over time (if interested, email off line and I'll send a draft) -- one can then cluster the entire "link-time" graph for temporally cohesive regions. Also on the notion of time-specific modularity; Peter Mucha and colleagues have a new partitioning algorithm out that takes temporal patterns directly into account. Best, Jim -----Original Message----- From: Social Networks Discussion Forum [mailto:[log in to unmask]] On Behalf Of Balazs Vedres Sent: Tuesday, January 05, 2010 9:38 AM To: [log in to unmask] Subject: Re: community identification algorithms ***** To join INSNA, visit http://www.insna.org ***** I see two concerns with current conceptions of cohesion: 1. Are overlaps allowed or made sense of? Most methods allow overlap, but in their application there is very little mentioned about the phenomenon of overlapping. Is it an accident? Does it have benefits? Are there meaningful connections to agency? 2. Does it work with dynamic data? Most methods are incapacitated in a dynamic framework. Let's say a dense, cohesive group A stays the same between t1 and t2 (same members, and the same ties among them). Now, most methods (including most new ones like Moody and White) would not recognize that group A stayed the same, if the network environment changes (it becomes denser or sparser for example). In other words, I see no other solution in a dynamic framework beyond local algorithms (like the clique percolation method). Otherwise how do you separate network change from methodological artifact? _____________________________________________________________________ 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.