gin

Graph Isomorphism Network (GIN)

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

Dependencies

• PyTorch 1.1.0+
• sklearn
• tqdm

bash pip install torch sklearn tqdm

How to run

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

python main.py

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

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

add --degree_as_nlabel to use one-hot encodings of node degrees as node feature vectors

Results

results may fluctuate, due to random factors and the relatively small data set. if you want to follow the paper's setting, consider the script below.

# 4 bioinformatics datasets setting graph_pooling_type=sum, the nodes have categorical input features 
python main.py --dataset MUTAG --device 0  \
                --graph_pooling_type sum --neighbor_pooling_type sum --filename MUTAG.txt

python main.py --dataset PTC --device 0  \
                --graph_pooling_type sum --neighbor_pooling_type sum --filename PTC.txt

python main.py --dataset NCI1 --device 0  \
                --graph_pooling_type sum --neighbor_pooling_type sum --filename NCI1.txt

python main.py --dataset PROTEINS --device 0  \
                --graph_pooling_type sum --neighbor_pooling_type sum --filename PROTEINS.txt

# 5 social network datasets setting graph_pooling_type=mean, for the REDDIT datasets, we set all node feature vectors to be the same 
# (thus, features here are uninformative); for the other social networks, we use one-hot encodings of node degrees.  
python main.py --dataset COLLAB --device 0  \
                --graph_pooling_type mean --neighbor_pooling_type sum --degree_as_nlabel --filename COLLAB.txt

python main.py --dataset IMDBBINARY --device 0  \
                --graph_pooling_type mean --neighbor_pooling_type sum --degree_as_nlabel --filename IMDBBINARY.txt

python main.py --dataset IMDBMULTI --device 0  \
                --graph_pooling_type mean --neighbor_pooling_type sum --degree_as_nlabel --filename IMDBMULTI.txt

python main.py --dataset REDDITBINARY --device 0  \
                --graph_pooling_type mean --neighbor_pooling_type sum --filename REDDITBINARY.txt --fold_idx 6 --epoch 120

python main.py --dataset REDDITMULTI5K --device 0  \
                --graph_pooling_type mean --neighbor_pooling_type sum --filename REDDITMULTI5K.txt

one fold of 10 result are below.

dataset	our result	paper report
MUTAG	89.4	89.4 ± 5.6
PTC	68.5	64.6 ± 7.0
NCI1	78.5	82.7 ± 1.7
PROTEINS	72.3	76.2 ± 2.8
COLLAB	81.6	80.2 ± 1.9
IMDBBINARY	73.0	75.1 ± 5.1
IMDBMULTI	54.0	52.3 ± 2.8
REDDITBINARY	88.0	92.4 ± 2.5
REDDITMULTI5K	54.8	57.5 ± 1.5