This example shows how to use modules defined in dgl.nn.pytorch.conv to do node classification on citation network datasets.

• Cora
• Citeseer
• Pubmed

• GCN: Semi-Supervised Classification with Graph Convolutional Networks
• GAT: Graph Attention Networks
• GraphSAGE Inductive Representation Learning on Large Graphs
• APPNP: Predict then Propagate: Graph Neural Networks meet Personalized PageRank
• GIN: How Powerful are Graph Neural Networks?
• TAGCN: Topology Adaptive Graph Convolutional Networks
• SGC: Simplifying Graph Convolutional Networks
• AGNN: Attention-based Graph Neural Network for Semi-supervised Learning
• ChebNet: Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

python run.py [--gpu GPU] --model MODEL_NAME --dataset DATASET_NAME [--self-loop]

The hyperparameters might not be the optimal, you could specify them manually in conf.py.