Explainability Techniques for Graph Convolutional Networks

Code and notebooks for the paper "Explainability Techniques for Graph Convolutional Networks" accepted at the ICML 2019 Workshop "Learning and Reasoning with Graph-Structured Data".

Overview

A Graph Network trained to predict the solubility of organic molecules is applied to sucrose, the prediction is explained using Layer-wise Relevance Propagation that assigns positive and negative relevance to the nodes and edges of the molecular graph:

The predicted solubility can be broken down to the individual features of the atoms and their bonds:

Code structure

• src, config, data contain code, configuration files and data for the experiments
  • infection, solubility contain the code for the two experiments in the paper
  • torchgraphs contain the core graph network library
  • guidedbackrprop, relevance contain the code to run Guided Backpropagation and Layer-wise Relevance Propagation on top of PyTorch's autograd
• notebooks, models contain a visualization of the datasets, the trained models and the results of our experiments
• test contains unit tests for the torchgraphs module (core GN library)
• conda.yaml contains the conda environment for the project

Setup

The project is build on top of Python 3.7, PyTorch 1.1+, torchgraphs 0.0.1 and many other open source projects.

A Conda environment for the project can be installed as:

conda env create -n gn-exp -f conda.yaml
conda activate gn-exp
python setup.py develop
pytest

Training

Detailed instructions for data processing, training and hyperparameter search can be found in the respective subfolders:

• Infection: infection/notes.md
• Solubility: solubility/notes.md

Experimental results

The results of our experiments are visualized through the notebooks in notebooks:

conda activate gn-exp
cd notebooks
jupyter lab