CS 69191: Masters Seminar
CS 89191: Doctoral Seminar
Spring 2009
Doctoral Student Presentation:
Partitioning for Signed Bipartite Graphs
Victor Lee
Bipartite graphs with signed edges are an interesting special case of
graphs that are becoming increasing prominent, because they model
social networks that describe a like/dislike or voting behavior. In
the case of a voting network, which has applications in both political
and consumer behavior, we have voter vertices, object vertices, and
edges with value either +1 or -1. Analysts are motivated to detected
trends in voter or consumer behavior. While weighted graphs are
well-studied, negatively-valued edges pose unique problems because
they invalidate many of the common mathematical techniques. We pose
the following research question: How can we effectively partition both
voter vertices and object vertices according to similar patterns of
edge connectivity? We stress that We propose two methods to solve
this problem: one based on K-means clustering, and one employing the
Expectation-Maximization (EM) probabilistic modeling technique. While
these methods are seemingly quite different, we seek to demonstrate
similarities in their mathematical foundations. We test the K-means
method on a sample of U.S. Senate votes.