Homophily-Enhanced Self-supervision (HES-GSL)

This is a PyTorch implementation of the Homophily-Enhanced Self-supervision (HES-GSL), and the code includes the following modules:

• Dataset Loader (Cora, Citeseer, Pubmed, ogbn-arxiv, Texas, Cornell, Wisconsin, Actor, MNIST, and Fashion-MNIST)

• Various supervision signals

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

• Visualization and evaluation metrics

Main Requirements

• networkx==2.5
• numpy==1.19.2
• scipy==1.3.1
• torch==1.6.0
• dgl==0.6.1

Description

• main.py

  • get_loss_classification() -- Calculate downstream supervision
  • get_loss_reconstruction() -- Calculate reconstruction-based self-supervision
  • main() -- Pre-train and fine-tune model for node classification task on ten real-world datasets

• model.py

  • GCN_CLA() -- GCN classifier
  • GSL() -- Learn a task-specific underlying graph structure
  • GCN_DAE() -- Denoising Autoencoder
  • get_loss_homophily() -- Calculate the homophily-enhanced self-supervision

• dataset.py

  • load_data() -- Load Cora, Citeseer, Pubmed, Texas, Cornell, Wisconsin, and Actor datasets
  • load_ogb_data() -- Load ogbn-arxiv dataset
  • load_mnist_data() -- Load MNIST dataset
  • load_fashionmnist_data() -- Load Fashion-MNIST dataset

• utils.py

  • get_random_mask() -- Add noise to input node features
  • top_k() -- Construct a kNN graph
  • knn_fast() -- Construct a kNN graph in a faster manner
  • get_homophily() -- Calculate the homophily ratio of the generated graph

Dataset

The datasets used in this paper are available in:

https://drive.google.com/file/d/1bCkI1fTmgHLHesPkyzUtAIriLPswZZ4f/view?usp=share_link

Running the code

1. Install the required dependency packages

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

python main.py --dataset data_name

where the data_name is one of the ten datasets (Cora, Citeseer, Pubmed, ogbn-arxiv, Texas, Cornell, Wisconsin, Actor, MNIST, and Fashion-MNIST). Use the Cora dataset as an example:

python main.py --dataset cora

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}
}

License

Homophily-Enhanced Self-supervision (HES-GSL) is released under the MIT license.