On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs (DLG-KDD 2021)
This repository is the official implementation of the paper. If you would like to use/modify the code or reproduce the experiment results, please remember to cite this paper:
Hejie Cui, Zijie Lu, Pan Li, Carl Yang: On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs. Proceedings of Workshop of Deep Learning on Graphs: Methods and Applications, The 27th International ACM SIGKDD Conference on Knowledge Discovery and Data Mining (DLG-KDD’21).
The experiments are run locally on Ubuntu 20.04, with NVCC 10.2 and Python 3.8.5. The Python module versions can be found in requirements.txt.
To run experiments, first set up virtualenv. After activating the virtul environment, run
pip3 install -r requirements.txt
cd graphsage-simple
bash train_nodes.sh
You can specify the initialization method, dataset, epoch, feature dimension and learning rate in train_nodes.sh.
The results can be found in results folder.
Refer to gnn_comparison/experiment.sh for example runbook.
Deepwalk is one of the initialization methods that we explore in this project. To generate deepwalk features, refer to https://github.com/phanein/deepwalk.
Make sure to include dataset directory in the --input and --ouput flag value, and set the --format.