The source code for Adaptive Kernel Graph Neural Network at AAAI2022

Related tags

Deep Learning AKGNN
Overview

AKGNN

The source code for Adaptive Kernel Graph Neural Network at AAAI2022.

Please cite our paper if you think our work is helpful to you:

@inproceedings{ju2022akgnn,
  title={Adaptive Kernel Graph Neural Network},
  author={Ju, Mingxuan and Hou, Shifu and Fan, Yujie and Zhao, Jianan and Ye, Yanfang and Zhao, Liang},
  booktitle={36th AAAI Conference on Artificial Intelligence (AAAI)},
  year={2022}
}

Requirements

  • Python 3.8.3
  • Please install other pakeages by pip install -r requirement.txt

Usage Example

  • Running on Cora: python train_cora.py
  • Running on Citeseer: python train_citeseer.py
  • Running on Pubmed: python train_pubmed.py

Results

Our model achieves the following accuracies on Cora, CiteSeer and Pubmed with the public splits:

Model name Cora CiteSeer Pubmed
AKGNN 84.8% 73.5% 80.4%

Running Environment

The experimental results reported in paper are conducted on a single NVIDIA GeForce RTX 2080 Ti with CUDA 11.1, which might be slightly inconsistent with the results induced by other platforms.

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