Community detection in networks by dynamical optimal transport formulation.

ABSTRACT: Detecting communities in networks is important in various domains of applications. While a variety of methods exist to perform this task, recent efforts propose Optimal Transport (OT) principles combined with the geometric notion of Ollivier-Ricci curvature to classify nodes into groups by rigorously comparing the information encoded into nodes' neighborhoods. We present an OT-based approach that exploits recent advances in OT theory to allow tuning between different transportation regimes. This allows for better control of the information shared between nodes' neighborhoods. As a result, our model can flexibly capture different types of network structures and thus increase performance accuracy in recovering communities, compared to standard OT-based formulations. We test the performance o

SUBMITTER: Leite D 

PROVIDER: S-EPMC9546897 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature