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We introduce a new class of latent process models for dynamic relationalnetwork data with the goal of detecting time-dependent structure. Network dataare often observed over time, and static network models for such data may failto capture relevant dynamic features. We present a new technique foridentifying the emergence or disappearance of distinct subpopulations ofvertices. In this formulation, a network is observed over time, with attributededges appearing at random times. At unknown time points, subgroups of verticesmay exhibit a change in behavior. Such changes may take the form of a change inthe overall probability of connection within or between subgroups, or a changein the distribution of edge attributes. A mixture distribution for latentvertex positions is used to detect heterogeneities in connectivity behaviorover time and over vertices. The probability of edges with various attributesat a given time is modeled using a latent-space stochastic process associatedwith each vertex. A random dot product model is used to describe the dependencystructure of the graph. As an application we analyze the Enron email corpus.

Detecting Time-dependent Structure in Network Data via a New Class of Latent Process Models