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Learning to Augment (L2A)

This is a PyTorch implementation of the Learning to Augment (L2A), and the code includes the following modules:

  • Dataset Loader (Cora, Citeseer, BlagCatalog, Texas, Cornell, Wisconsin, Actor, and Syn-Cora)

  • Various Architectures (GCN, SAGE, GAT, and GNN Classifier used in this paper)

  • Training paradigm pre-training and fine-tuning on 8 datasets

  • Visualization and evaluation metrics

Main Requirements

  • networkx==2.5
  • numpy==1.19.2
  • scipy==1.5.2
  • torch==1.6.0
  • pyro_ppl==1.3.0

Description

  • main.py

    • pretrain_EdgePredictor() -- Pretrain Graph Augmentor
    • pretrain_Classifier() -- Pretrain GNN Classifier
    • main() -- Train the model for node classification task on the Cora, Citeseer, BlagCatalog, Texas, Cornell, Wisconsin, Actor, and Syn-Cora datasets

  • model.py

    • GCNLayer() -- GCN Layer
    • SageConv() -- SAGE Layer
    • GATLayer() -- GAT Layer
    • EdgePredictor() -- Learn parameterized augmentation distribution
    • EdgeSampler() -- Perform gumbel-softmax sampling
    • EdgeLearning() -- Learn weighted graph
    • Classifier() -- Classify nodes based on the learned weighted graph

  • graphSSL.py

    • DistanceCluster() -- Perform self-supervised Global-Path Prediction
    • ContextLabel() -- Perform self-supervised Local Label Distribution Preservation

  • dataset.py

    • load_data() -- Load synthetic and real-world datasets

  • utils.py

    • evaluation() -- Calculate classification accuracy

Running the code

  1. Install the required dependency packages

  2. To get the results on a specific dataset with a specific GNN architecture, please run with proper hyperparameters:

python main.py --dataset data_name --model architecture

where the data_name is one of the 8 datasets (Cora, Citeseer, BlagCatalog, Texas, Cornell, Wisconsin, Actor, and Syn-Cora) and architecture is one of the 4 GNN architectures (GCN, SAGE, GAT, and GNN Classifier used in this paper). Use GCN on the Cora dataset an example:

python main.py --dataset cora --model GCN

Citation

If you are interested in our repository and our paper, please cite the following paper:

@inproceedings{wu2023learning,
  title={Learning to Augment Graph Structure for both Homophily and Heterophily Graphs},
  author={Wu, Lirong and Tan, Cheng and Liu, Zihan and Gao, Zhangyang and Lin, Haitao and Li, Stan Z},
  booktitle={Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
  pages={3--18},
  year={2023},
  organization={Springer}
}

Acknowledgement

This project borrows the architecture design and part of the code from GAUG.

License

Learning to Augment (L2A) is released under the MIT license.