Lecture: Bayesian Models of Social Networks and Text
Prof. Andrew McCallum from the UMass Department of Computer Science will deliver this lecture as part of the Fall 2006 Operations Research / Management Science Seminar series. All are invited to attend.
Title: Bayesian Models of Social Networks and Text
Abstract: 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.
This series is organized by the UMass Amherst INFORMS Student Chapter. Support for this series is provided by the Isenberg School of Management, the Department of Finance and Operations Management, and the John F. Smith Memorial Fund.
