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https://github.com/baldassarrefe/graph-network-explainability

Explainability techniques for Graph Networks, applied to a synthetic dataset and an organic chemistry task. Code for the workshop paper "Explainability Techniques for Graph Convolutional Networks" (ICML19)

https://github.com/baldassarrefe/graph-network-explainability

Science Score: 23.0%

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Keywords

artificial-intelligence bioinformatics explainability graph-networks
Last synced: 5 months ago · JSON representation

Repository

Explainability techniques for Graph Networks, applied to a synthetic dataset and an organic chemistry task. Code for the workshop paper "Explainability Techniques for Graph Convolutional Networks" (ICML19)

Basic Info
  • Host: GitHub
  • Owner: baldassarreFe
  • Language: Jupyter Notebook
  • Default Branch: master
  • Homepage:
  • Size: 6.48 MB
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  • Forks: 16
  • Open Issues: 3
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Topics
artificial-intelligence bioinformatics explainability graph-networks
Created almost 7 years ago · Last pushed over 6 years ago
Metadata Files
Readme

README.md

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:

Sucrose solubility LRP

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

Sucrose solubility LRP nodes Sucrose solubility LRP edges

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: bash 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: bash conda activate gn-exp cd notebooks jupyter lab

Owner

  • Name: Federico Baldassarre
  • Login: baldassarreFe
  • Kind: user
  • Location: Stockholm
  • Company: KTH

Passionate about AI, data science, and SW Engineering, BSc in Computer Engineering @unibo Bologna, MSc in Machine Learning + PhD candidate at @KTH Stockholm

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