For people who prefer python, here is the pytorch implementation of s2v:
https://github.com/Hanjun-Dai/pytorch_structure2vec
(Doxygen) http://www.cc.gatech.edu/~hdai8/graphnn/html/annotated.html
Tested under Ubuntu 14.04, 16.04 and Mac OSX 10.12.6
Download and install cuda from https://developer.nvidia.com/cuda-toolkit
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_8.0.44-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1404_8.0.44-1_amd64.deb
sudo apt-get update
sudo apt-get install cuda
in .bashrc, add the following path (suppose you installed to the default path)
export CUDA_HOME=/usr/local/cuda
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH
in .bashrc, add the following path
source {path_to_your_intel_root/name_of_parallel_tool_box}/bin/psxevars.sh
Dockerfile contains all the required installations (including Intel MKL and TBB) above. Only additional requirement is to provide NVIDIA*.run script that will load the same NVIDIA driver of host into the target. Then to build the container, execute:
docker build -t "graphnn:test" .
To run it:
docker run --runtime=nvidia graphnn:test bash
If above command fails for a reason, refer to https://github.com/NVIDIA/nvidia-docker. If no error occurs, you can simply follow the below instructions and execute them in the container without failure.
cp make_common.example make_common
modify configurations in make_common file
make -j8
cd examples/mnist
make
./run.sh
cd examples/graph_classification
make
./local_run.sh
The 5 datasets under the data/ folder are commonly used in graph kernel.
@article{dai2016discriminative,
title={Discriminative Embeddings of Latent Variable Models for Structured Data},
author={Dai, Hanjun and Dai, Bo and Song, Le},
journal={arXiv preprint arXiv:1603.05629},
year={2016}
}
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