  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.
