Revisiting Heterophily for Graph Neural Networks (Sitao Luan et al., NeurIPS 2022): https://arxiv.org/abs/2210.07606

Updated manuscript with additional results on large-scale datasets (Penn94, arXiv-year, genius, twitch-gamers). See Table 4 in Appendix A.3.

Homophily metrics	t-SNE visualization of GCN and ACM-GCN layers trained on Squirrel

We provide the PyTorch implementation for ACM-GNN framework here. The repository is organised as follows:

|-- ACM-PyTorch # experiments on 10 small-scale datasets
    |-- experiment/ # experiment bash script 
    |-- models/ # model definition
    |-- splits/ # split files for datasets
    |-- arg_parser.py  # the argument parser code for training/hyperparameter searching script
    |-- hyperparameter_searching.py # the hyperparameter searching script
    |-- logger.py # the logger code
    |-- train.py # the model trainig script
    |-- utils.py # data process and others
|-- ACM-Geometric # experiments on the large-scale and small-scale datasets based on the data provided by LINKX
    |-- sh/ # experiment bash script
    |-- large_scale_data/ # dataset folder
    |-- train.py # the model trainig script
    |-- parse.py  # set hyper-parameters
|-- data/ # 3 old datasets, including cora, citeseer, and pubmed
|-- new-data/ # 6 new datasets, including texas, wisconsin, cornell, actor, squirrel, and chameleon
|-- synthetic-experiments # generate synthetic features and graphs with different homophily levels and train baseline models
    |-- baseline_models/ # models and layers of baseline models
    |-- feature_generation.py  # generate node features with base datasets
    |-- graph_generation.py  # generate graphs with different homophily levels
    |-- train.py # the training code
    |-- utils.py # data process and others
    |-- hyperparameter_searching.py # searching the hyperparameters of the baseline model
    |-- logger.py # logger
    |-- homophily.py # different homophily metrics
|-- plots # all experimental plots and visualizations in our paper

The script has been tested running under Python 3.7.4, with the following packages installed (along with their dependencies):

• dgl-cpu==0.4.3.post2
• dgl-gpu==0.4.3.post2
• ogb==1.3.1
• numpy==1.19.2
• scipy==1.4.1
• networkx==2.5
• torch==1.5.0
• torch-cluster==1.5.7
• torch-geometric==1.6.3
• torch-scatter==2.0.5
• torch-sparse==0.6.6
• torch-spline-conv==1.2.0

In addition, CUDA 10.2 has been used.

pip install -r requirements.txt

# training with default hyperparameters (e.g. ACM-GCN+ on Texas)
python train.py --model acmgcnp --dataset_name texas

# training with user defined hyperparameters
python train.py --model acmgcnp --dataset_name texas --lr 0.06 --weight_decay 0.0006 --dropout 0.6

# hyperparameter searching (learning rate, weight_decay & dropout)
python hyperparameter_searching.py --model acmgcnp --dataset_name texas

The training/hyperparameter seacrhing logs are saved into the logs/ folder located at <your-working-directory>/Adaptive-Channel-Mixing-GNN/ACM-PyTorch/logs.

Create a results/ folder at <your-working-directory>/Adaptive-Channel-Mixing-GNN/ACM-Geometric. Experimental results (.csv) would be saved here.

Download the data/ folder from the repository of LINKX to <your-working-directory>/Adaptive-Channel-Mixing-GNN/ACM-Geometric/large_scale_data.

# training with default hyperparameters (e.g. ACM-GCN+ on twitch-gamer)
python train.py --model acmgcnp --dataset_name "twitch-gamer"

# training with user defined hyperparameters
python train.py --model acmgcnp --dataset_name "twitch-gamer" --lr 0.002 --weight_decay 0.0006 --dropout 0.6

# hyperparameter searching
bash sh/run_all_settings.sh

# generate features
python feature_generation.py --base_dataset cora

# generate (random or regular) graphs
python graph_generation.py --graph_type random --degree_intra 2 --num_graph 10

Generated features are saved into the synthetic_graphs/features/ folder located at <your-working-directory>/Adaptive-Channel-Mixing-GNN/synthetic-experiment/synthetic_graphs/.

Generated graphs are saved into the synthetic_graphs/<random|regular>/ folder located at <your-working-directory>/Adaptive-Channel-Mixing-GNN/synthetic-experiment/synthetic_graphs/<random|regular>.

# training with user defined hyperparameters
python train.py --model_type acmgcn --base_dataset cora --graph_type random --edge_homo 0.1 --degree_intra 2 --num_graph 10 --lr 0.05 --weight_decay 0.0005 --dropout 0.5

# hyperparameter searching (weight_decay & dropout)
python hyperparameter_searching.py --model_type acmgcn --graph_type random --base_dataset cora --edge_homo 0.1 --edge_homo 0.1 --degree_intra 2 --num_graph 10 

Graph generation and training logs are saved into the logs/ folder located at <your-working-directory>/Adaptive-Channel-Mixing-GNN/synthetic-experiment/logs.

See the leaderboards for node classification on heterophilic graphs here. Feel free to add your results or create new benchmark datasets.

Parts of the code are based on

• GCN

• LINKX

If you make advantage of the ACM framework in your research, please cite the following in your manuscript:

@article{luan2022revisiting,
  title={Revisiting heterophily for graph neural networks},
  author={Luan, Sitao and Hua, Chenqing and Lu, Qincheng and Zhu, Jiaqi and Zhao, Mingde and Zhang, Shuyuan and Chang, Xiao-Wen and Precup, Doina},
  journal={Advances in neural information processing systems},
  volume={35},
  pages={1362--1375},
  year={2022}
}

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