sgbmm / graphqembed Goto Github PK

Code for making predictions about logical queries using network embeddings and for reproducing the results of the paper "Querying Complex Networks in Vector Space."

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.

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.