Research map / Graph learning / Graph self-supervised learning
vGraph: A Generative Model for Joint Community Detection and Node Representation Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add smoothness regularization to vGraph (ablation) | vGraph · Overlapping community detection benchmarks | F1-score or Jaccard Similarity: facebook0 F1-score: 0.2440 -> 0.2606; facebook414 F1-score: 0.6471 -> 0.6693; results decrease on facebook348 F1-score: | Mixed |
| Jointly learn node representations and community membership | vGraph · Non-overlapping community detection | NMI: vGraph outperforms conventional node embeddings + K-Means in 4 out of 6 datasets | Mixed |
| Jointly learn node representations and community membership | vGraph · Non-overlapping community detection | Modularity: vGraph outperforms all 6 conventional node embeddings + K-Means datasets | Helped |
| Jointly learn node representations and community membership | vGraph · Node classification | Micro-F1 or Macro-F1: vGraph significantly outperforms all baseline methods in 9 out of 12 datasets | Helped |
Closest papers by meaning
- Graph Representation Learning via Ladder Gamma Variational Autoencoders (AAAI 2020 · similarity 0.90)
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- Community Detection Guarantees using Embeddings Learned by Node2Vec (NeurIPS 2024 · similarity 0.88)
- HiGen: Hierarchical Graph Generative Networks (ICLR 2024 · similarity 0.88)
- PieClam: A Universal Graph Autoencoder Based on Overlapping Inclusive and Exclusive Communities (ICML 2025 · similarity 0.87)
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