slowbull / stochastic_gcn

Stochastic training of graph convolutional networks

Stochastic Training for Graph Convolutional Networks

Code for the paper Stochastic Training for Graph Convolutional Networks. The implementation is based on Thomas Kipf's implementation for graph convolutional networks.

Requirements

• tensorflow (>0.12)
• networkx (<=1.11 for loading GraphSAGE datasets)

Installation

Make sure that you have valid c++11 and cuda compiler.

pip install --upgrade pip
pip install numpy scipy networkx==1.11 tensorflow-gpu scikit-learn
python setup.py install
cd gcn
./build.sh

Data

We support both GCN's data format and GraphSAGE's data format.

To download the Citeseer, Cora, PubMed, PPI and Reddit dataset

mkdir data
cd data
git clone [e-mail]:tkipf/gcn.git
mv gcn/gcn/data/* .

wget http://snap.stanford.edu/graphsage/ppi.zip
unzip ppi.zip
mv ppi/* .

wget http://snap.stanford.edu/graphsage/reddit.zip
unzip reddit.zip
mv reddit/* .

Run the demo

We provide recipes for the Citeseer, Cora, PubMed, NELL, PPI, and Reddit dataset in the config folder. Please refer to our paper for the algorithms. Typically, CVD+PP has similar accuracy with Exact, but is faster.

# Exact algorithm
config/cora.config
# NS+PP algorithm
config/cora.config --degree=1 --test_degree=1
# CV+PP algorithm
config/cora.config --cv --test_cv --degree=1 --test_degree=1 
# CVD+PP algorithm
config/cora.config --cv --cvd --test_cv --degree=1 --test_degree=1

Cite

Please cite our paper if you use this code in your own work:

@article{chen2017stochastic,
  title={Stochastic Training of Graph Convolutional Networks},
  author={Chen, Jianfei and Zhu, Jun},
  journal={arXiv preprint arXiv:1710.10568},
  year={2017}
}