PyGCN（Graph Convolutional Nueral Network）图卷积网络的算法实现

Graph Convolutional Networks in PyTorch ==== 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](http://tkipf.github.io/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]. ## Installation ```python setup.py install``` ## Requirements * PyTorch 0.4 or 0.5 * Python 2.7 or 3.6 ## Usage ```python train.py``` ## References [1] [Kipf & Welling, Semi-Supervised Classification with Graph Convolutional Networks, 2016](https://arxiv.org/abs/1609.02907) [2] [Sen et al., Collective Classification in Network Data, AI Magazine 2008](http://linqs.cs.umd.edu/projects/projects/lbc/) ## Cite 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} } ```