Simultaneous Identification of Sparse Structures and Communities in Heterogeneous Graphical Models

arXiv:2405.09841v1 Announce Type: cross
Abstract: Exploring and detecting community structures hold significant importance in genetics, social sciences, neuroscience, and finance. Especially in graphical models, community detection can encourage the exploration of sets of variables with group-like properties. In this paper, within the framework of Gaussian graphical models, we introduce a novel decomposition of the underlying graphical structure into a sparse part and low-rank diagonal blocks (non-overlapped communities). We illustrate the significance of this decomposition through two modeling perspectives and propose …

abstract arxiv communities community cs.lg detection exploration finance framework genetics identification importance neuroscience paper social social sciences stat.ml type variables