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

Journal: STAR protocols
Published Date: Nov 24, 2022

Abstract

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)..

Authors

  • Andrea Mastropietro
    Department of Computer, Control and Management Engineering Antonio Ruberti (DIAG) Sapienza University of Rome, 00185 Rome, Italy; Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, 53115 Bonn, Germany. Electronic address: mastropietro@diag.uniroma1.it.
  • Giuseppe Pasculli
    Nephrology, Dialysis, and Transplantation Unit, Department of Emergency and Organ Transplantation, University of Bari Aldo Moro, Bari, Italy.
  • Jürgen Bajorath
    Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2, D-53113 Bonn, Germany.

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