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7 March 2007

Bayesian Models of Social Networks and Text with Application to Political, Legal and Bibliometric Data

This is an abstract of this weeks PNG/CCCSN seminar with Andrew McCallum (University of Massachusetts, Amherst). We encourage you to discuss his presentation via comments on the blog.

"The field of social network analysis studies mathematical models of patterns in the interactions between people or other entities. In this talk I will present several recent advances in generative, probabilistic modeling of networks and their per-edge attributes. The Author-Recipient-Topic model discovers role-similarity between entities by examining not only network connectivity, but also the words communicated on on those edges; I'll demonstrate this method on a large corpus of email data subpoenaed as part of the Enron investigation. The Group-Topic model discovers groups of entities and the "topical" conditions under which different groupings arise; I'll demonstrate this on coalition discovery from many years worth of voting records in the U.S. Senate and the U.N. I'll conclude with further examples of Bayesian networks successfully applied to relational data, as well as discussion of their applicability to trend analysis, expert-finding and bibliometrics."

Here is a link to Andrew's talk: "Bayesian Models of Social Networks and Text with Application to Political, Legal and Bibliometric Data"

Posted by Bernie Cahill at March 7, 2007 10:15 AM

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