Research map / Graph learning / Graph self-supervised learning
Community Preserving Network Embedding
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add modularity-based community structure term (ablation) | NMF network embedding (M-NMF0) · Texas | clustering accuracy: 47.06 -> 63.10 | Helped |
| add modularity-based community structure term (ablation) | M-NMF0 · Washington | classification accuracy: 54.67 -> 63.56 | Helped |
| M-NMF vs best baseline embeddings | network embedding · 9 networks | clustering accuracy | Mixed |
| M-NMF vs best baseline embeddings | network embedding · Texas | classification accuracy: 73.16 -> 70.00 | Hurt |
| vary alpha and beta in {0.1..10} (ablation) | M-NMF · Washington, Wisconsin | accuracy | No clear effect |
| vary number of communities k from 3 to 13 (ablation) | M-NMF · Washington, Wisconsin | accuracy | No clear effect |
Closest papers by meaning
- Incorporating Network Embedding into Markov Random Field for Better Community Detection (AAAI 2019 · similarity 0.92)
- Multi-Facet Network Embedding: Beyond the General Solution of Detection and Representation (AAAI 2018 · similarity 0.92)
- Community Detection Guarantees using Embeddings Learned by Node2Vec (NeurIPS 2024 · similarity 0.90)
- Learning Persistent Community Structures in Dynamic Networks via Topological Data Analysis (AAAI 2024 · similarity 0.89)
- How Low Can You Go? Searching for the Intrinsic Dimensionality of Complex Networks using Metric Node Embeddings (ICLR 2025 · similarity 0.87)
- Non-Euclidean Mixture Model for Social Network Embedding (NeurIPS 2024 · similarity 0.86)
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