LirongWu / RFA-GNN

Code for TNNLS paper "Beyond Homophily and Homogeneity Assumption: Relation-based Frequency Adaptive Graph Neural Networks"

Relation-based Frequency Adaptive Graph Neural Networks (RFA-GNN)

This is a PyTorch implementation of the RFA-GNN, and the code includes the following modules:

• Datasets (Cora, Citeseer, Pubmed, Texas, Cornell, Wisconsin, Film, Chameleon, Squirrel, Syn-Cora, Syn-Relation, and ZINC)

• Training paradigm for node classification, graph classification, and graph regression tasks on 12 datasets

• Visualization

• Evaluation metrics

Main Requirements

• dgl==0.5.3
• networkx==2.5
• numpy==1.19.2
• matplotlib==3.1.1
• scikit-learn==0.24.1
• scipy==1.5.2
• torch==1.6.0

Description

• train.py

  • main() -- Train a new model for node classification task on the Cora, Citeseer, Pubmed, Texas, Cornell, Wisconsin, Film, Chameleon, Squirrel, and Syn-Cora datasets
  • accuracy() -- Test the learned model for node classification task on the Cora, Citeseer, Pubmed, Texas, Cornell, Wisconsin, Film, Chameleon, Squirrel, and Syn-Cora datasets
  • main_synthetic() -- Train a new model for graph classification task on the Syn-Relation dataset
  • evaluate_synthetic() -- Test the learned model for graph classification task on the Syn-Relation dataset
  • main_zinc() -- Train a new model for graph regression task on the ZINC datasets
  • evaluate_zinc() -- Test the learned model for graph regression task on the ZINC datasets

• dataset.py

  • preprocess_data() -- Load data of selected dataset

• model_RFAGCN.py

  • RFAGNN() -- model and loss

• utils.py

  • evaluate_graph() -- Evaluate relation-learning performance with the visualization of the learned relation graphs

Running the code

1. Install the required dependency packages

2. We use DGL to implement all the GCN models (and their modules) on 12 datasets. The three citation datasets (Cora, Citeseer, and Pubmed) are provided by the DGL library; the Syn-relation and Syn-cora datasets are self-generated by the provided code dataset.py; the ZINC dataset and the remainding six heterophily datasets are downloaded from the Google Drive.

3. To get the results on a specific dataset, run with proper hyperparameters

python train.py --dataset data_name

where the data_name is one of the 12 datasets (Cora, Citeseer, Pubmed, Texas, Cornell, Wisconsin, Film, Chameleon, Squirrel, Syn-relation, Syn-cora, and Zinc). The model as well as the training log will be saved to the corresponding dir in ./log for evaluation.

4. The evaluation the performance of three-level disentanglement performance, run

python utils.py

Citation

If you find this project useful for your research, please use the following BibTeX entry.

@article{wu2023beyond,
  title={Beyond homophily and homogeneity assumption: Relation-based frequency adaptive graph neural networks},
  author={Wu, Lirong and Lin, Haitao and Hu, Bozhen and Tan, Cheng and Gao, Zhangyang and Liu, Zicheng and Li, Stan Z},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2023},
  publisher={IEEE}
}