Graph Feature Networks (GFN)

This is the implementation of GFN used in paper "Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification", previously titled "Dissecting Graph Neural Networks on Graph Classification". GFN is a simple lightweight neural set function with graph augmented features. It augments nodes with graph structural and propagated features, and then defines the function directly on a set of nodes. See our paper for more details.

Requirements

• pytorch_geometric

This code was developed under version 1.1.0. So if it doesn't work for your pytorch_geometric, you may want to install the right version.

Run the experiments

Replace the benchmark experiment below with whichever you want to run, see all exps in main.py.

python main --exp benchmark

Cite

Please cite our paper if you find it helpful in your own work:

@article{gfn2019,
  title={Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification},
  author={Ting Chen, Song Bian, Yizhou Sun}
  journal={CoRR},
  volume={abs/1905.04579},
  year={2019},
}