Protocol to explain graph neural network predictions using an edge-centric Shapley value-based approach

Abstract

Summary: Here we present EdgeSHAPer, a workflow for explaining graph neural networks by approximating Shapley values using Monte Carlo sampling. In this protocol, we describe steps to execute Python scripts for a chemical dataset from the original publication; however, this approach is also applicable to any user-provided dataset. We also detail steps encompassing neural network training, an explanation phase, and analysis via feature mapping.For complete details on the use and execution of this protocol, please refer to Mastropietro et al. (2022).1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Keywords

• Bioinformatics
• Chemistry
• Computer sciences