This is the repository for paper GraphAIR: Graph Representation Learning with Neighborhood Aggregation and Interaction. This paper has been submitted to Patten Recognition (Elsevier).
- Tensorflow (1.9.0)
- networkx
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.
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 0.5python train.py --dataset citeseer --epochs 3500 --w1 1.1 --w2 0.6 --w3 0.6python train.py --dataset pubmed --epochs 400 --w1 1.1 --w2 0.9 --w3 0.6Please cite our paper if you use this code in your own work:
@article{GraphAIR19,
title = {GraphAIR: Graph Representation Learning with Neighborhood Aggregation and Interaction},
author = {Fenyu Hu and Yanqiao Zhu and Shu Wu and Weiran Huang and Liang Wang and Tieniu Tan},
journal = {arXiv preprint arXiv:1911.01731},
year = {2019}
}
The structure of this code is largely based on GCN by Kipf.