Many recent scientific efforts have been devoted to constructing the human connectome using Diffusion Tensor Imaging (DTI) data for understanding large-scale brain networks that underlie higher-level cognition in human. However, suitable network analysis computational tools are still lacking in human brain connectivity research. To address this problem, we propose a novel probabilistic multi-graph decomposition model to identify consistent network modules from the brain connectivity networks of the studied subjects. At first, we propose a new probabilistic graph decomposition model to address the high computational complexity issue in existing stochastic block models. After that, we further extend our new probabilistic graph decomposition model for multiple networks/graphs to identify the shared modules cross multiple brain networks by simultaneously incorporating multiple networks and predicting the hidden block state variables. We also derive an efficient optimization algorithm to solve the proposed objective and estimate the model parameters. We validate our method by analyzing both the weighted fiber connectivity networks constructed from DTI images and the standard human face image clustering benchmark data sets. The promising empirical results demonstrate the superior performance of our proposed method.
Keywords: Human Connectome; Multi-Graph Decomposition; Probabilistic Graph Decomposition.