Description

- Graph Neural Networks (GNNs) for Graph Classification

• Implementation of various neural graph classification model (not node classification)
• Training and test of various Graph Neural Networks (GNNs) models using graph classification datasets
• Input graph: graph adjacency matrix, graph node features matrix
• Graph classification model (graph aggregating)
  • Get latent graph node featrue matrix
  • GCN, GAT, GIN, ...
• Readout: transforming each latent node feature to one dimension vector for graph classification
• Feature modeling: fully-connected layer

How to use

- Details of parameter: references to help of argparse

python train.py --model_list GCN GAT --dataset_list ALL --readout_list ALL --n_agg_layer 2 -- agg_hidden 32

Contents

- Available Model

• Graph Convolutional Networks (GCNs): https://arxiv.org/abs/1609.02907
• GraphSAGE: https://arxiv.org/pdf/1810.05997.pdf
• Simple Graph Convolutional Networks (SGCNs): https://arxiv.org/pdf/1902.07153.pdf
• Graph Attention Networks (GATs): https://arxiv.org/abs/1710.10903
• Graph UNet: https://arxiv.org/pdf/1905.05178.pdf
• Approximate Personalized Propagation of Neural Predictions (APPNP): https://arxiv.org/pdf/1810.05997.pdf
• Graph Isomorphism Networks (GINs): https://arxiv.org/pdf/1810.00826.pdf
• Graph Neural Networks with Convolutional ARMA Filters: https://arxiv.org/pdf/1901.01343.pdf
• Graph ResNet
• Graph DenseNet
• Node RandomWalk Network
• Expanded Spatial Graph Embedding Network

- Available Datasets

• Node labels X, edge labels X: IMDB-BINARY, IMDB-MULTI
• Node labels O, edge labels X: PROTEINS, ENZYMES, NCI1
• Node labels O, edge labels O: MUTAG

- Available Readout

• Basic readout: max, avg, sum

References

- Graph classification data processing

https://github.com/bknyaz/graph_nn

Author

- LinkedIn: https://www.linkedin.com/in/taeyong-kong-016bb2154

- Blog URL: https://blog.naver.com/qbxlvnf11

- Email: [email protected], [email protected]