Research map / Graph learning / Graph algorithms and clustering
Community Detection in Attributed Graphs: An Embedding Approach
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use community structure embedding matrix plus attribute NMF (CDE) instead of adjacency-based NMF | NMF community detection · Wisconsin | NMI: 0.1823 -> 0.4284 | Helped |
| CDE vs best baseline | overlapping community detection · Facebook ego-networks, Philosophers, Flickr | F1-score: 0.1014 -> 0.1326 | Helped |
| CDE vs CESNA | overlapping community detection · FB ego-network 3437 | Jaccard: 0.1311 -> 0.1272 | Hurt |
| tune negative samples kappa to 22-29 (ablation) | CDE · Wisconsin | AC | Helped |
| set beta=2 instead of 1 (more weight on structure embedding) (ablation) | CDE · Wisconsin | AC: 0.6645 -> 0.7321 | Helped |
Closest papers by meaning
- Semantic Community Identification in Large Attribute Networks (AAAI 2016 · similarity 0.91)
- Community Detection Guarantees using Embeddings Learned by Node2Vec (NeurIPS 2024 · similarity 0.89)
- Learning Persistent Community Structures in Dynamic Networks via Topological Data Analysis (AAAI 2024 · similarity 0.88)
- Scalable Attribute-Missing Graph Clustering via Neighborhood Differentiation (ICML 2025 · similarity 0.87)
- PieClam: A Universal Graph Autoencoder Based on Overlapping Inclusive and Exclusive Communities (ICML 2025 · similarity 0.87)
- DGCLUSTER: A Neural Framework for Attributed Graph Clustering via Modularity Maximization (AAAI 2024 · similarity 0.87)
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