This is the repository for paper GraphAIR: Graph Representation Learning with Neighborhood Aggregation and Interaction. This paper has been accepted by Patten Recognition (Elsevier).
conda create -n air python=3.6
conda activate air
pip install -r requirements.txt
We prove that existing neighborhood aggregation scheme has difficulty in well capturing complicated non-linearity of graph data. Our work explicitly models neighborhood interaction for better capturing non-linearity of node features.
The architecture is illustrated as follows.
You can conduct node classification experiments on citation network (Cora, Citeseer or Pubmed) using the following commands:
python train.py --dataset cora --epochs 400 --w1 1.1 --w2 0.5 --w3 0.5
python train.py --dataset citeseer --epochs 3500 --w1 1.1 --w2 0.6 --w3 0.6
python train.py --dataset pubmed --epochs 400 --w1 1.1 --w2 0.9 --w3 0.6
Please cite our paper if you use this code in your own work:
@article{hu2020graphair,
title={Graphair: Graph representation learning with neighborhood aggregation and interaction},
author={Hu, Fenyu and Zhu, Yanqiao and Wu, Shu and Huang, Weiran and Wang, Liang and Tan, Tieniu},
journal={Pattern Recognition},
pages={107745},
year={2020},
publisher={Elsevier}
}
The structure of this code is largely based on GCN by Kipf.