We present a new generative model for relational data in which relations between objects can have either a binding or a separating effect. For example, in a group of students separated into gender clusters, a "dating" relation would appear most frequently between the clusters, but a "roommate" relation would appear more often within clusters. In visualizing these relations, one can imagine that the "dating" relation effectively pushes clusters apart, while the "roommate" relation pulls clusters into tighter formations. A unique aspect of the model is that an edge's existence is dependent on both the clusters to which the two connected objects belong and the features of the connected objects. We use simulated annealing to search for optimal values of the unknown model parameters, where the objective function is a Bayesian score derived from the generative model. This paper provides a short description of the research, including a brief discussion of future directions.
Subjects: 12. Machine Learning and Discovery; 3.4 Probabilistic Reasoning
Submitted: Mar 31, 2008