Wesfeiler-Lehman Neural Machine (WLNM) is a subgraph-based link prediction method leveraging deep learning to automatically learn graph structure features for link prediction from links' enclosing subgraphs. For more information, see the following paper:
M. Zhang and Y. Chen, Weisfeiler-Lehman Neural Machine for Link Prediction, Proc. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-17). [Video] [PDF]
Run Main.m in MATLAB to do the link prediction experiments.
You need to install liblinear for saving .mat data to libsvm format so that Torch can read them.
cd software/
wget -O liblinear.tar.gz http://www.csie.ntu.edu.tw/~cjlin/cgi-bin/liblinear.cgi?+http://www.csie.ntu.edu.tw/~cjlin/liblinear+tar.gz
tar xvzf liblinear.tar.gz
cd liblinear-2.1/
make
This will install liblinear. Then, in MATLAB, cd to "software/liblinear-2.1/matlab/", type "make" to install the MATLAB interface. Please also change the names of "train.mexa64" and "predict.mexa64" to "liblinear_train.mexa64" and "liblinear_predict.mexa64" respectively if you want to train a logistic regression model instead of neural network.
The "WLNM.m" file contains three models: 1) logistic regression, 2) neural network by Torch, and 3) neural network by MATLAB. Please change model in the file according to your need.
If you want to run the Torch neural network (has the best performance, and features GPU acceleration), you can follow the steps below to install cuda and Torch (for those who do not have deep learning environment in your machine):
Install cuda following: http://developer.nvidia.com/cuda-downloads
Install Torch following this link: http://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions
Then, install necessary Torch libraries: cutorch, cunn, in order by:
luarocks install --local libraryName
Now, you should be able to use the Torch neural network model in "nDNN.lua".
In MATLAB, type
Main
to run the experiments. By default, you will run WLNM on the smallest network, USAir, for 1 time. Modify: numberOfExperiment, dataname, and method in "Main.m", in order to run repeated experiments on all datasets, and compare all link prediction methods.
If your MATLAB cannot succesfully load cunn, please try the following command to start MATLAB. This will replace the MATLAB's libstdc++.so.6 with the system's default one (for successfully loading cunn of Torch inside MATLAB).
LD_PRELOAD="/usr/lib64/libstdc++.so.6" matlab
If your MATLAB prints "cannot load ....../....../libcutorch.so", (that is because MATLAB replaces system's environment variables with its own ones when executing command 'system'.) please check your system's LD_LIBRARY_PATH by typing the following command in your shell:
echo $LD_LIBRARY_PATH
Then, in MATLAB, before running Main, type
setenv LD_LIBRARY_PATH what_you_got_from_the_echo_command
in order to recall system's LD_LIBRARY_PATH. After that, you should be able to run Torch scripts within MATLAB successfully.
You may also modify "WLNM.m" in order to only store the training and testing data without running Torch, and use your own neural network programs to train and test on them.
Torch libraries nnsparse and svm are required to load data in libsvm format. Install them by:
luarocks install --local nnsparse
luarocks install --local svm
MATLAB Toolbox Bioinformatics is required to calculate graph shortest distance in "graph2vector.m".
To calculate AUC score inside lua, you need to download the metrics package (optional)
git clone https://github.com/hpenedones/metrics.git
cd metrics
luarocks --local make
We use nauty, a graph canonization software to break ties in palette-wl labelings. This toolbox also contains a useful MATLAB interface for nauty.
To use nauty, first download nauty26r7.tar.gz into "software/" by running:
cd software/
wget http://pallini.di.uniroma1.it/nauty26r7.tar.gz
Then install nauty by:
tar xvzf nauty26r7.tar.gz
cd nauty26r7
./configure
make
There are two MATLAB wrappers canon.m, canonical.c in the main folder. If there is no canonical.mex file in the main folder, the program canon.m will automatically compile canonical.c for the first time.
If you find the code useful, please cite our paper:
@inproceedings{zhang2017weisfeiler,
title={Weisfeiler-Lehman Neural Machine for Link Prediction},
author={Zhang, Muhan and Chen, Yixin},
booktitle={Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages={575--583},
year={2017},
organization={ACM}
}
Thanks!
For any questions regarding the paper, the code, the installation and so on, please raise issues or email me. I am happy to help you.
Muhan Zhang, muhan@wustl.edu 2/15/2017