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Official repository for ICLR'23 paper: Multi-task Self-supervised Graph Neural Network Enable Stronger Task Generalization

Hi all, this is the official repository for ICLR 2023 paper: Multi-task Self-supervised Graph Neural Network Enable Stronger Task Generalization. Our paper can be found at [OpenReview link]. We sincerely apprecaite your interests in our projects!

Instruction

To reproduce our experiment, you need to first run the python code hetero_graph_gen.py to generate the DGL graph objects for heterophilous graphs (i.e., Chameleon, Actor, and Squirrel) and link_gen.py to prepare training/validation/testing splits for the evaluation on link prediciton. The link_gen.py may require an environment with 128GB RAM due to the size of ogb-product graph. If you have limited RAM and are not interested in running experiments about ogb-product, you can comment out its link generation code block.

You can finish these two steps by simply run commands as follows:

python hetero_graph_gen.py
python link_gen.py

Then to conduct multi-task self-supervised learning, we have prepared bash scripts in the scripts directory. Training a ParetoGNN can be done by:

cd scripts

\\ to generate node embeddings for node-level tasks like node classification, node clustering, and partition prediction
bash ssnc_{dataset}.sh <GPU_ID> 

\\ to generate node embeddings for link prediction
bash link_{dataset}.sh <GPU_ID> 

The node embeddings and the final model will be saved in the scripts directory.

To conduct evaluation, please run our evaluation script:

python eval.py --data {dataset} --embedding_path_node {path_to_emb_node} --embedding_path_link {path_to_emb_link}

Dependencies and Environments

The package we use include:

* DGL 0.9.0
* PyTorch 1.12.0
* ogb 1.3.4

We aim at training our models on GPUs with 24 GB VRAM. Examplary bash scripts we provide should run without problems on 24 GB GPUs.

Cite

If you find this repository useful in your research, please cite our paper:

@article{ju2023multi,
  title={Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization},
  author={Ju, Mingxuan and Zhao, Tong and Wen, Qianlong and Yu, Wenhao and Shah, Neil and Ye, Yanfang and Zhang, Chuxu},
  booktitle={International Conference on Learning Representations},
  year={2023}
}

Credit

Some scripts about the pre-text tasks come from the example directory from the official repository of DGL. And some data generation scripts for link prediction come from the CFLP repo.

Contact

Mingxuan Ju (mju2@nd.edu)