pygcn-master
所属分类：其他
开发工具：Python
文件大小：235KB
下载次数：11
上传日期：2019-08-18 15:43:23
上 传 者：
shu_
说明： 图卷积神经网络，图神经网络的一种，通过提取特征来训练神经网络，实现网络嵌入
(Graph Convolution Neural Network, a kind of Graph Neural Network, trains the Neural Network by extracting features to realize network embedding.)
文件列表:
LICENCE (1071, 2019-02-26)
data (0, 2019-07-28)
data\cora (0, 2019-07-28)
data\cora\cora.cites (69928, 2019-02-26)
data\cora\cora.content (7823427, 2019-02-26)
figure.png (49982, 2019-02-26)
pygcn (0, 2019-07-28)
pygcn\__init__.py (135, 2019-02-26)
pygcn\__pycache__ (0, 2019-07-29)
pygcn\__pycache__\__init__.cpython-37.pyc (288, 2019-07-28)
pygcn\__pycache__\layers.cpython-37.pyc (1611, 2019-07-29)
pygcn\__pycache__\models.cpython-37.pyc (934, 2019-07-29)
pygcn\__pycache__\utils.cpython-37.pyc (2873, 2019-07-28)
pygcn\layers.py (1482, 2019-07-29)
pygcn\models.py (896, 2019-07-29)
pygcn\train.py (4057, 2019-07-29)
pygcn\utils.py (2848, 2019-02-26)
setup.py (553, 2019-02-26)
该代码是kipf与2019提供的官方实现版本。
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}
}
```
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