- Paper link: arXiv OpenReview
- Author's code repo: https://github.com/weihua916/powerful-gnns.
- MXNet 1.5+
- sklearn
- tqdm
bash pip install torch sklearn tqdm
An experiment on the GIN in default settings can be run with
DGLBACKEND=mxnet python main.py
An experiment on the GIN in customized settings can be run with
DGLBACKEND=mxnet python main.py [--device 0 | --disable-cuda] --dataset COLLAB \
--graph_pooling_type max --neighbor_pooling_type sum
Run with following with the double SUM pooling way: (tested dataset: "MUTAG"(default), "COLLAB", "IMDBBINARY", "IMDBMULTI")
DGLBACKEND=mxnet python main.py --dataset MUTAG --device 0 \
--graph_pooling_type sum --neighbor_pooling_type sum