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There are several important social influence and contagion modeling
approaches that you should look at. Noah Friedkin's book readily comes
to mind. Good earlier sources are the chapters on diffusion and social
influence in the Wasserman and Galaskiewicz 1994 book. Tom Valente's
chapter in the more recent Carrington volume reviews diffusion of
innovation research.

In the p* tradition, we did some work on social influence models a
little while ago: Robins, G.L., Pattison, P, & Elliott, P. (2001).
Network models for social influence processes. Psychometrika, 66,
161-190.  Our group has started to update some of these models and
Galina Daraganova will be talking about these more recent developments
at sunbelt in Corfu.

Garry Robins
Department of Psychology
School of Behavioural Science
University of Melbourne
Victoria 3010

Tel: 61 3 8344 4454
Fax: 61 3 9347 6618
Melnet website:

-----Original Message-----
From: Social Networks Discussion Forum [mailto:[log in to unmask]] On
Behalf Of Vaclav Petricek
Sent: Friday, 16 March 2007 3:56 AM
To: [log in to unmask]
Subject: predicting attribute data using network structure

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As I understand, the p1, p2, p* models are used to model network ties as

independent variable, possibly using actor attributes as explanatory 

Could you please point me to work/method that addresses the reverse 
problem of modeling attribute data from the network structure? In this 
case the attributes are dependent variables and should be explained by 
the network ties plus attribute data of alters and other actors in the 
network. I am especially interested in predicting categorical data with 
large number of categories. My background is in computer science and I 
am looking at using social networks for collaborative filtering. I would

be interested in how social network scientists have approached or would 
approach this problem.

I'll post a summary of the answers to the list - please let me know if 
you want me to keep your reply private.

Thank you for your advice,


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