Research map / Graph learning / Graph self-supervised learning
Diffusion-Convolutional Neural Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace feature-only/structure-only baselines with a 2-hop diffusion-convolutional network (node features plus diffusion over graph) | node classification · Cora and Pubmed | Accuracy: 0.8677 vs 0.8449 (CRF-LBP best baseline) on Cora; 0.8976 vs 0.8718 (l1logistic) on Pubmed | Helped |
| Use diffusion-convolution (graph diffusion power series) instead of graph-only kernels | node classification · Cora | Accuracy: 0.8677 vs 0.8229 (KLED) | Helped |
| Increase diffusion breadth from 0 to about 3 hops then plateau (ablation) | 2-hop/multi-hop DCNN · node classification (Cora) | Accuracy | Helped |
| Use DCNN for whole-graph classification (mean pooling of node activations) | graph classification · NCI1, NCI109, MUTAG, PTC, ENZYMES | Accuracy: NCI1 5-hop 0.6261 vs deepwl 0.6215; ENZYMES 5-hop 0.1810 vs deepwl 0.2155 | No clear effect |
| Increase search breadth H for whole-graph classification (ablation) | graph classification · graph classification datasets | Accuracy | No clear effect |
Closest papers by meaning
- Data Representation and Learning With Graph Diffusion-Embedding Networks (CVPR 2019 · similarity 0.92)
- Convolutional Kernel Networks for Graph-Structured Data (ICML 2020 · similarity 0.91)
- Learning to Approximate Adaptive Kernel Convolution on Graphs (AAAI 2024 · similarity 0.90)
- A Generalized Neural Diffusion Framework on Graphs (AAAI 2024 · similarity 0.88)
- Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter Tuning (ICLR 2025 · similarity 0.88)
- Unifying Generation and Prediction on Graphs with Latent Graph Diffusion (NeurIPS 2024 · similarity 0.88)
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