Research map / Graph learning / GNN architectures
Powerful Graph Convolutional Networks with Adaptive Propagation Mechanism for Homophily and Heterophily
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use adaptive homophily-guided propagation | HOG-GCN · Texas | accuracy: 84.59$\pm$4.37 -> 85.17$\pm$4.40 | Helped |
| Use adaptive homophily-guided propagation | HOG-GCN · Wisconsin | accuracy: 85.49$\pm$3.53 -> 86.67$\pm$3.36 | Helped |
| Use adaptive homophily-guided propagation | HOG-GCN · Cornell | accuracy: 83.24$\pm$7.03 -> 84.32$\pm$4.32 | Helped |
| Use adaptive homophily-guided propagation | HOG-GCN · Film | accuracy: 36.73$\pm$1.03 -> 36.82$\pm$0.84 | Helped |
| Use adaptive homophily-guided propagation | HOG-GCN · Cora | accuracy: 87.18$\pm$1.13 -> 87.04$\pm$1.10 | Hurt |
| Use adaptive homophily-guided propagation | HOG-GCN · Citeseer | accuracy: 76.01$\pm$1.90 -> 76.15$\pm$1.88 | Helped |
| Use adaptive homophily-guided propagation | HOG-GCN · Pubmed | accuracy: 89.08$\pm$0.67 -> 88.79$\pm$0.40 | Hurt |
| Use 2-hop rather than 1-hop neighborhoods (ablation) | HOG-GCN · Texas and Cornell | accuracy | Helped |
| Use 2-hop rather than 1-hop neighborhoods (ablation) | HOG-GCN · Cora and Citeseer | accuracy | Helped |
| Increase neighborhood order from 2 to 6 (ablation) | HOG-GCN · Cora and Citeseer | accuracy | Hurt |
| Combine attribute- and topology-derived homophily estimates (ablation) | HOG-GCN · Texas | accuracy | Helped |
| Use either the attribute or topology homophily estimate alone (ablation) | HOG-GCN · Texas | accuracy | Hurt |
Closest papers by meaning
- Refining Latent Homophilic Structures over Heterophilic Graphs for Robust Graph Convolution Networks (AAAI 2024 · similarity 0.92)
- PC-Conv: Unifying Homophily and Heterophily with Two-Fold Filtering (AAAI 2024 · similarity 0.91)
- Making Classic GNNs Strong Baselines Across Varying Homophily: A Smoothness–Generalization Perspective (NeurIPS 2025 · similarity 0.91)
- Integrating Co-Training with Edge Discrimination to Enhance Graph Neural Networks Under Heterophily (AAAI 2025 · similarity 0.90)
- Understanding Heterophily for Graph Neural Networks (ICML 2024 · similarity 0.90)
- What Is Missing For Graph Homophily? Disentangling Graph Homophily For Graph Neural Networks (NeurIPS 2024 · similarity 0.89)
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