Graph Convolutional Network (GCN)is a powerful neural network designed for machine learning on graphs. Based on PGL, we reproduce GCN algorithms and reach the same level of indicators as the paper in citation network benchmarks.
图卷积网络（GCN）是一个功能强大的神经网络，旨在用于图上的机器学习。基于PGL，我们重现了GCN算法，并达到了与引用网络基准中的论文相同的指标水平。
To build a gcn layer, one can use our pre-definedpgl.layers.gcnor just write a gcn layer with message passing interface.
数据集
The datasets contain three citation networks: CORA, PUBMED, CITESEER. The details for these three datasets can be found in thepaper.
本demo展示了在CORA, PUBMED, CITESEER数据集上面的表现，数据集的详细介绍可以看论文【Semi-Supervised Classification with Graph Convolutional Networks】
需要的包
paddlepaddle>=1.6
飞桨1.6版本
pgl
飞桨图神经网络学习框架
We train our models for 200 epochs and report the accuracy on the test dataset.
DatasetAccuracy
Cora~81%
Pubmed~79%
Citeseer~71%
如何去运行
For examples, use gpu to train gcn on cora dataset.
例如，你可以用下面的命令在GPU上开始你的训练
python train.py --dataset cora --use_cuda
参数
dataset: The citation dataset “cora”, “citeseer”, “pubmed”.
配置数据集，可选数据集有cora citeseer pubmed
use_cuda: Use gpu if assign use_cuda.
是否使用GPU加速科学计算
数据集介绍
Semi-Supervised Classification with Graph Convolutional Networks
