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UCoDe: unified community detection with graph convolutional networks
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Journal Article
UCoDe: unified community detection with graph convolutional networks
2023
Overview
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.
Publisher
Springer US,Springer Nature B.V
Subject
