/examples/pytorch/gin

Graph Isomorphism Network (GIN)

• Paper link: arXiv OpenReview
• Author's code repo: https://github.com/weihua916/powerful-gnns.

Dependencies

• PyTorch 1.0.1+
• sklearn
• tqdm

bash pip install torch sklearn tqdm

How to run

An experiment on the GIN in default settings can be run with

python3 main.py

An experiment on the GIN in customized settings can be run with

python3 main.py [--device 0 | --disable-cuda] --dataset COLLAB \
               --graph_pooling_type max --neighbor_pooling_type sum

Results

Run with following with the double SUM pooling way: (tested dataset: "MUTAG"(default), "COLLAB", "IMDBBINARY", "IMDBMULTI")

python3 train.py --dataset MUTAB --device 0  \
                --graph_pooling_type sum --neighbor_pooling_type sum

• MUTAG: 0.85 (paper: ~0.89)
• COLLAB: 0.89 (paper: ~0.80)
• IMDBBINARY: 0.76 (paper: ~0.75)
• IMDBMULTI: 0.51 (paper: ~0.52)