An implementation of the GSR model proposed in Self-Supervised Graph Structure Refinement for Graph Neural Network in WSDM23. Please kindly note that this work is inspired by the great work GAuG, especially the adding and removing edges part.

• Python >=3.8.5
• Pytorch == 1.8.1
• DGL == 0.6.2
• SciPy == 1.6.2
• NetworkX==2.5.1

• Step 1 : Unzip the data.zip file
• Step 2: Run the experiments using the command below

to recover our results, specify "-l" option

python root_to_src_dir/src/models/GSR/trainGSR.py -dcora -l

• dataset: The dataset to run on.
• gpu: GPU id to use.
• train_percentage:The train ratio, 0 for default split.
• load_default_config: Whether load default config or use parsed config.
• early_stop: Number of epoch for early stop.
• seed: Training seed.

• epochs: The maximum epoch to train in the fine-tuning process.
• intra_weight: The weight of intra contrastive loss, $\alpha$ in the paper.
• fsim_weight: The weight of feature similarity in estimating edge probability, $\beta$ in the paper.
• add_ratio: Percentage of non-existing edge to add in the graph refinement process.
• rm_ratio: Percentage of existing edge to remove in the graph refinement process.
• fan_out: Number of neighbors to sample in first and second order subgraphs.
• p_epochs: Number of pre-training epochs.
• p_batch_size: The pre-training batch size.
• prt_lr: The pre-training learning rate.

If you find our work useful, please consider citing our work:

@inproceeding{zhao2023gsr,
  title={Self-Supervised Graph Structure Refinement for Graph Neural Networks},
  author = {Jianan Zhao and Qianlong Wen and Mingxuan Ju and Chuxu Zhang and Yanfang Ye},
  booktitle = {WSDM},
  year      = {2023},
}