Wiener Graph Deconvolutional Network Improves Self-Supervised Learning

This is the official implementation of the following paper:

Wiener Graph Deconvolutional Network Improves Self-Supervised Learning
Jiashun Cheng, Man Li, Jia Li, Fugee Tsung; AAAI 2023

Dependencies

• python >= 3.7
• torch >= 1.11.0
• torch_geometric >= 2.0.3
• ogb >= 1.3.4
• argparse >= 1.1.0
• numpy >= 1.12.2
• scikit_learn >= 1.0.2
• scipy >= 1.4.1

Quick Start

To reproduce the reported results, please run the script with --use_cfg.

Node classification

# With best configurations
python main_node.py --dataset PubMed --use_cfg

# Or you can customize the configurations (such as propagation kernel, decoder aggregation and etc.)
python main_node.py --dataset PubMed --kernel heat --dec_aggr sum 

Supported datasets includes Cora, CiteSeer, PubMed, CS, Physics, Computers, Photo, ogbn-arxiv

Graph classification

# With best configurations
python main_graph.py --dataset IMDB_BINARY --use_cfg --seed 2 12 22 32 42

# Or you can customize the configurations (such as propagation kernel, decoder aggregation, pooler and etc.)
python main_node.py --dataset IMDB_BINARY --kernel heat --dec_aggr sum --pooler max --seed 2 12 22 32 42

Supported datasets includes IMDB-BINARY, IMDB-MULTI, PROTEINS, COLLAB, DD, NCI1

Citing

If you find this work is helpful to your research, please consider citing our paper:

@article{cheng2022latent,
  title={Latent Augmentation For Better Graph Self-Supervised Learning},
  author={Cheng, Jiashun and Li, Man and Li, Jia and Tsung, Fugee},
  journal={arXiv preprint arXiv:2206.12933},
  year={2022}
}