UCoDe: unified community detection with graph convolutional networks
Atefeh Moradan, Andrew Draganov, Davide Mottin, Ira Assent
Abstract
Abstract Community detection finds homogeneous groups of nodes in a graph. Existing approaches either partition the graph into disjoint, non-overlapping , communities, or determine only overlapping communities. To date, no method supports both detections of overlapping and non-overlapping communities. We propose UCoDe, a unified method for community detection in attributed graphs that detects both overlapping and non-overlapping communities by means of a novel contrastive loss that captures node similarity on a macro-scale. Our thorough experimental assessment on real data shows that, regardless of the data distribution, our method is either the top performer or among the top performers in both overlapping and non-overlapping detection without burdensome hyper-parameter tuning.