Simple and Deep Graph Convolutional Networks
Graph convolutional networks (GCNs) are a powerful deep learning approach for graph-structured data. Recently, GCNs and subsequent variants have shown superior performance in various application areas on real-world datasets. Despite their success, most of the current GCN models are shallow, due to the {\em over-smoothing} problem. In this paper, we study the problem of designing and analyzing deep graph convolutional networks. We propose the GCNII, an extension of the vanilla GCN model with two simple yet effective techniques: {\em Initial residual} and {\em Identity mapping}. We provide theoretical and empirical evidence that the two techniques effectively relieves the problem of over-smoothing. Our experiments show that the deep GCNII model outperforms the state-of-the-art methods on various semi- and full-supervised tasks. Code is available at https://github.com/chennnM/GCNII .
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Tasks
OGB
Cora
| Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
|---|---|---|---|---|---|---|
| Node Classification | Citeseer Full-supervised | GCNII* | Accuracy | 77.13% | # 5 | |
| Node Classification | CiteSeer with Public Split: fixed 20 nodes per class | GCNII | Accuracy | 73.4% | # 9 | |
| Node Classification | Cora Full-supervised | GCNII | Accuracy | 88.49% | # 1 | |
| Node Classification | Cora with Public Split: fixed 20 nodes per class | GCNII | Accuracy | 85.5% | # 1 | |
| Node Property Prediction | ogbn-arxiv | GCNII | Test Accuracy | 0.7274 ± 0.0016 | # 35 | |
| Validation Accuracy | Please tell us | # 57 | ||||
| Number of params | 2148648 | # 7 | ||||
| Ext. data | No | # 1 | ||||
| Node Classification | PPI | GCNII* | F1 | 99.56 | # 1 | |
| Node Classification | Pubmed Full-supervised | GCNII* | Accuracy | 90.30% | # 4 | |
| Node Classification | PubMed with Public Split: fixed 20 nodes per class | GCNII | Accuracy | 80.2% | # 12 |
