myerson 1.0.1

A python package to calculate Myerson values from game theory and use them as explanations for graph neural networks.

Calculate Myerson values and explain GNNs

This package implements the Myerson solution concept from cooperative game theory. The Myerson values attribute every player of a game their fair contribution to the games payoff. Myerson values are related to Shapley values but the player cooperation is restricted by a graph.

A graph neural network (GNN) can be treated as a coalition function for a game and the Myerson values can be used as feature attribution explanations to understand a model prediction. This package also implements Methods to explain PyG GNNs with Myerson values.

Calculating the Myerson value scales exponentially with bigger graphs / more players. Therfore, Monte Carlo sampling techniques were implemented to approximate the Myerson values.

Installation

Install the complete package with PyTorch dependencies using one of the following commands:

# pip
pip install myerson[explain]

# conda / mamba
conda install myerson

# for conda / mamba, you need to manually install pytorch dependencies
conda install pytorch torchvision torchaudio cpuonly -c pytorch
conda install conda install pyg -c pyg

If you are only interested in the game theoretic part you don't need to install PyTorch:

# pip 
pip install myerson

# conda / mamba
conda install myerson

Examples and Documentation

Example uses can be found here. The full documentation can be found at https://myerson.readthedocs.io/.

Results of Explaining GNNs for Cheminformatics

This package was developed to explain GNN predicitons for Cheminformatics approaches. The repository with code and data for these experiments can be found here: https://github.com/kochgroup/myerson_results.

Citation

TBD.