Common and Individual Structure of Multiple Networks
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This article focuses on the problem of studying shared- and individual-specific structure in replicated networks or graph-valued data. In particular, the observed data consist of n graphs, G_i, i=1,...,n, with each graph consisting of a collection of edges between V nodes. In brain connectomics, the graph for an individual corresponds to a set of interconnections among brain regions. Such data can be organized as a V × V binary adjacency matrix A_i for each i, with ones indicating an edge between a pair of nodes and zeros indicating no edge. When nodes have a shared meaning across replicates i=1,...,n, it becomes of substantial interest to study similarities and differences in the adjacency matrices. To address this problem, we propose a method to estimate a common structure and low-dimensional individual-specific deviations from replicated networks. The proposed Multiple GRAph Factorization (M-GRAF) model relies on a logistic regression mapping combined with a hierarchical singular value decomposition. We develop an efficient algorithm for maximum likelihood estimation, and study basic properties of our approach. Simulation studies show excellent operating characteristics and we apply the method to human brain connectomics data.
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