Graph Neural Networks with Random Features (SDM 2021)

We prove that adding random features to each node strengthens the expressive power of graph neural networks. The complete paper including appendices is available on arXiv.

This repository is forked from https://github.com/weihua916/powerful-gnns

Installation

Install PyTorch following the instuctions on the [official website] (https://pytorch.org/). The code has been tested over PyTorch 0.4.1 and 1.0.0 versions.

Then install the other dependencies.

pip install -r requirements.txt

Test run

$ bash exec.sh

Datasets

We release new synthetic benchmarks we used in the paper. All datasets are stored in the dataset directory. Each file describes a set of graphs with the following format:

s 0
G_1
G_2
...
G_s

where s is the number of graphs, there is 0 for compatibility with graph classification datasets, G_i describes a graph with the following format:

n
y_1 d_1 v_{1, 1} v_{1, 2} ... v_{1, d_1}
y_2 d_2 v_{2, 1} v_{2, 2} ... v_{2, d_2}
...
y_n d_n v_{n, 1} v_{n, 2} ... v_{n, d_n}

where n is the number of nodes, the (i+2)-th line describes node i (node ids are 0-indexed), y_i is the label of node i, d_i is the degree of node i, v_{i, j} is the j-th neighbor of node i.

You can also generate (more) synthetic datasets by dataset_gen.py.

The MDS datasets do not contain the label information (i.e., y_i = 0 for all i). We generate labels dynamically using algorithms.py when we test models.

Feedback and Contact

Please feel free to contact me at r.sato AT ml.ist.i.kyoto-u.ac.jp, or to open issues.

Citation

@inproceedings{sato2021random,
  author    = {Ryoma Sato and Makoto Yamada and Hisashi Kashima},
  title     = {Random Features Strengthen Graph Neural Networks},
  booktitle = {Proceedings of the 2021 {SIAM} International Conference on Data Mining, {SDM}},
  year      = {2021},
}