# UCoDe: Unified Community Detection with Graph Convolutional Networks

arXiv: https://arxiv.org/abs/2112.14822 · HTML: https://mlsanity.com/paper/2112.14822/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2021.

## Built on by (most cited first, 1 of 1)

- [A Local Perspective-based Model for Overlapping Community Detection](https://mlsanity.com/paper/2503.21558/) (2025)

## Abstract (arXiv)

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.