karl-crl/decagon

Graph convolutional neural network for multirelational link prediction

Decagon: Representation Learning on Multimodal Graphs

Author: Marinka Zitnik (marinka@cs.stanford.edu)

Project website

Overview

This repository contains code necessary to run the Decagon algorithm. Code in original repo contains mistakes, so we want to make it runnable on article data.

Requirements

Code required all packeges from file requirements.txt (latest version preferable) and TensorFlow 2.

Running the code

The setup for the polypharmacy problem on a synthetic dataset is outlined in main.py. It uses a small synthetic network example with five edge types. Run the code as following:

$ python main.py

The full polypharmacy dataset (described in the paper) is available on the project website. To run the code on the full dataset first download all data files from the project website. The polypharmacy dataset is already preprocessed and ready to use. After cloning the project, replace the synthetic example in main.py with the polypharmacy dataset and run the model.