GSKN: Theoretically Improving Graph Neural Networks via Anonymous Walk Graph Kernels

The repository implements GSKN described in the following paper

GSKN: Theoretically Improving Graph Neural Networks via Anonymous Walk Graph Kernels (WWW 2021, Research Track, Full Paper)

For more details, please see our Paper.

Installation

We strongly recommend users to use miniconda to install the following packages

python=3.6.2
numpy
scikit-learn=0.21
pytorch=1.3.1
torchvision=0.4.2
pandas
networkx
Cython
cyanure

All the above packages can be installed with conda install except cyanure, which can be installed with pip install cyanure-mkl.

CUDA Toolkit also needs to be downloaded with the same version as used in Pytorch. Then place it under the path $PATH_TO_CUDA and run export CUDA_HOME=$PATH_TO_CUDA.

Finally run make, and it may take few minutes to compile.

Data

Run cd dataset; bash get_data.sh to download and unzip datasets. We provide here 3 types of datasets: datasets without node attributes (IMDBBINARY, IMDBMULTI, COLLAB), datasets with discrete node attributes (MUTAG, PROTEINS, PTC) and datasets with continuous node attributes (BZR, COX2, PROTEINS_full). All the datasets can be downloaded and extracted from this site.

run

export PYTHONPATH=$PWD:$PYTHONPATH
python main.py --dataset MUTAG  --sigma 1.5 --hidden_size 16  --aggregation --anonymous_walk_length 6 --anonymous_walks_per_node 30

Acknowledgments

Certain parts of this project are partially derived from GCKN and GraphSTONE.