PyTorch implementation of Graph Convolutional Networks (GCNs) for semi-supervised classification [1].
For a high-level introduction to GCNs, see:
Thomas Kipf, Graph Convolutional Networks (2016)
Note: There are subtle differences between the TensorFlow implementation in https://github.com/tkipf/gcn and this PyTorch re-implementation. This re-implementation serves as a proof of concept and is not intended for reproduction of the results reported in [1].
This implementation makes use of the Cora dataset from [2].
这个项目是利用论文之间的引用关系,论文的文字出现的次数,希望预测论文的类别。
我从未想到原理复杂的GCN竟然有如此简洁的表达--Elliott Zheng
python setup.py install
python train.py
[1] Kipf & Welling, Semi-Supervised Classification with Graph Convolutional Networks, 2016
[2] Sen et al., Collective Classification in Network Data, AI Magazine 2008
Please cite our paper if you use this code in your own work:
@article{kipf2016semi,
title={Semi-Supervised Classification with Graph Convolutional Networks},
author={Kipf, Thomas N and Welling, Max},
journal={arXiv preprint arXiv:1609.02907},
year={2016}
}
此处可能存在不合适展示的内容,页面不予展示。您可通过相关编辑功能自查并修改。
如您确认内容无涉及 不当用语 / 纯广告导流 / 暴力 / 低俗色情 / 侵权 / 盗版 / 虚假 / 无价值内容或违法国家有关法律法规的内容,可点击提交进行申诉,我们将尽快为您处理。