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Overlapping Community Detection with Graph Neural Networks

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arxiv 1909.12201 v1 pith:K5NOXNRI submitted 2019-09-26 cs.LG cs.SIstat.ML

Oleksandr Shchur , Stephan Günnemann This is my paper · ORCID
classification cs.LGcs.SIstat.ML
keywords communitydetectionmodelneuraloverlappingcommunitiesexistinggraph
verification ladder T0 review T1 audit T2 compute T3 formal
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Community detection is a fundamental problem in machine learning. While deep learning has shown great promise in many graphrelated tasks, developing neural models for community detection has received surprisingly little attention. The few existing approaches focus on detecting disjoint communities, even though communities in real graphs are well known to be overlapping. We address this shortcoming and propose a graph neural network (GNN) based model for overlapping community detection. Despite its simplicity, our model outperforms the existing baselines by a large margin in the task of community recovery. We establish through an extensive experimental evaluation that the proposed model is effective, scalable and robust to hyperparameter settings. We also perform an ablation study that confirms that GNN is the key ingredient to the power of the proposed model.

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Forward citations

Cited by 5 Pith papers

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  4. ChemHGNN: A Hierarchical Hypergraph Neural Network for Reaction Virtual Screening and Discovery

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    A hypergraph neural network with reaction-center-aware negative sampling achieves higher F1 scores than GCN/GAT/HGNN baselines for reaction virtual screening on USPTO subsets.

  5. Methodology for Identifying Social Groups within a Transactional Graph

    cs.SI 2025-02 reject novelty 4.0 of 10

    A new framework defines SubGraphs of Interest and proposes selection and reverse-pruning methods to find social groups in transaction graphs, without any experimental validation.

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