The Multiple Random Dot Product Graph Model

Abstract

Data in the form of graphs, or networks, arise naturally in a number of contexts; examples include social networks and biological networks. We are often faced with the availability of multiple graphs on a single set of nodes. In this article, we propose the multiple random dot product graph model for this setting. Our proposed model leads naturally to an optimization problem, which we solve using an efficient alternating minimization approach. We further use this model as the basis for a new test for the hypothesis that the graphs come from a single distribution, versus the alternative that they are drawn from different distributions. We evaluate the performance of both the fitting algorithm and the hypothesis test in several simulation settings, and demonstrate empirical improvement over existing approaches. We apply these new approaches to a Wikipedia data set and a C. elegans data set.

References in corpus (1)

• Modeling homophily and stochastic equivalence in symmetric relational data

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• Bias-Variance Tradeoffs in Joint Spectral Embeddings