Vinayak Rao

Guilherme Gomes, Vinayak Rao, Jennifer Neville

Clustering and community detection with multiple graphs have typically focused on aligned graphs, where there is a mapping between nodes across the graphs (e.g., multi-view, multi-layer, temporal graphs). However, there are numerous application areas with multiple graphs that are only partially aligned, or even unaligned. These graphs are often drawn from the same population, with communities of potentially different sizes that exhibit similar structure. In this paper, we develop a joint stochastic blockmodel (Joint SBM) to estimate shared communities across sets of heterogeneous non-aligned graphs. We derive an efficient spectral clustering approach to learn the parameters of the joint SBM. We evaluate the model on both synthetic and real-world datasets and show that the joint model is able to exploit cross-graph information to better estimate the communities compared to learning separate SBMs on each individual graph.

Aditi Iyer, Bingjing Tang, Vinayak Rao et al.

We propose a novel two-phase approach to functional network estimation of multi-subject functional Magnetic Resonance Imaging (fMRI) data, which applies model-based image segmentation to determine a group-representative connectivity map. In our approach, we first improve clustering-based Independent Component Analysis (ICA) to generate maps of components occurring consistently across subjects, and then estimate the group-representative map through MAP-MRF (Maximum a priori - Markov random field) labeling. For the latter, we provide a novel and efficient variational Bayes algorithm. We study the performance of the proposed method using synthesized data following a theoretical model, and demonstrate its viability in blind extraction of group-representative functional networks using simulated fMRI data. We anticipate the proposed method will be applied in identifying common neuronal characteristics in a population, and could be further extended to real-world clinical diagnosis.