GraphQEmbed
Maintainer: William L. Hamilton ([email protected])
Code for making predictions about logical queries using network embeddings and for reproducing the results of the paper "Querying Complex Networks in Vector Space."
Setup and requirements
Run pip install -r requirements.txt to obtain the necessary requirements.
The primary requirements is pytorch with version >=3.0.
You may want to use a virtualenv or Docker.
The biological interaction network data used in the paper can be downloaded here. Unzip the data in your working directory.
Running the code
To train a model on the Bio data, run python -m nqe.bio.train.
See that file for a list of possible arguments, and note that by default it assumes that the data is in a subdirectory of your working directory (i.e., "./bio_data).
By default the model will log its output and store a version of the model after training.
The train, test, and validation performance will be recorded in the log file.
If you are training with a GPU be sure to add the cuda flag, i.e., python -m nqe.bio.train --cuda.
The default parameters correspond to the best performing variant from the paper.
NB: Currently the training files are not-portable pickle files. We hope to release a more portable version of the data soon.
NB: Only the bio data is currently publicly available.