This repository contains the code to compute graph representations that are complete in expectation. If you use this code in your work or find it useful in any other way, please consider citing our paper:
Pascal Welke*, Maximilian Thiessen*, and Thomas Gärtner: Expectation Complete Graph Representations Using Graph Homomorphisms.
[presented at GLFrontiers@NeurIPS2022] [presented at LOG2022]
Ensure that you have python installed, cmake, and a recent c++ compiler available. Currently, java is also a dependency, but it is superfluous.
To run the code in this repository, clone it somewhere and initialize the git submodules
git clone git@github.com:pwelke/homcount.git
cd homcount
git submodule init
git submodule update
To compile c++ part, enter the HomSub and compile the code
cd HomSub
git submodule init
git submodule update
sh build-third-party.sh
sh build.sh
Create a virtual environment, using python 3.7 (!) and install dependencies, e.g. with anaconda:
cd graph-homomorphism-network
conda create -n expectation_complete python==3.7
conda activate expectation_complete
pip install -r requirements_dev.txt
- Download data from here and unzip it into
graph-homomorphism-network/data. - Run (in the virtual environment)
python experiments.py, to only compute the embeddings of the selected datasets (if not already done) and save them ingraph-homomorphism-network/data/precompute. - Run (in the virtual environment)
python evaluation.py, to compute a number of embeddings of the selected datasets (if not already done) and save them ingraph-homomorphism-network/data/precompute. After that, run 10-fold cross validations for the MLP and SVM classifiers. - Note that there is currently a race condition with a temp file in . Hence, you cannot run multiple experiments simultaneously on the project folder. A workaroud would be to copy the full project folder multiple times.
- Currently, the average accuracies have to be manually collected from the output of evaluation.py.