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https://github.com/lirongwu/rfa-gnn

Code for TNNLS paper "Beyond Homophily and Homogeneity Assumption: Relation-based Frequency Adaptive Graph Neural Networks"
https://github.com/lirongwu/rfa-gnn

graph-algorithms graph-representation-learning heterogeneous-graph-learning heterophily-graph relation-learning

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

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README 

        
# 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](https://www.dgl.ai/) 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](https://www.dgl.ai/) 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](https://drive.google.com/file/d/1p3pMblv7eMRLtB4LERHwt8a50VvHp64n/view?usp=sharing).



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}

}

```