Quantifying the reproducibility of graph neural networks using multigraph data representation

Ahmed Nebli, Mohammed Amine Gharsallaoui, Zeynep Gürler, Islem Rekik (Lead / Corresponding author), Alzheimer's Disease Neuroimaging Initiative

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Graph neural networks (GNNs) have witnessed an unprecedented proliferation in tackling several problems in computer vision, computer-aided diagnosis and related fields. While prior studies have focused on boosting the model accuracy, quantifying the reproducibility of the most discriminative features identified by GNNs is still an intact problem that yields concerns about their reliability in clinical applications in particular. Specifically, the reproducibility of biological markers across clinical datasets and distribution shifts across classes (e.g., healthy and disordered brains) is of paramount importance in revealing the underpinning mechanisms of diseases as well as propelling the development of personalized treatment. Motivated by these issues, we propose, for the first time, reproducibility-based GNN selection (RG-Select), a framework for GNN reproducibility assessment via the quantification of the most discriminative features (i.e., biomarkers) shared between different models. To ascertain the soundness of our framework, the reproducibility assessment embraces variations of different factors such as training strategies and data perturbations. Despite these challenges, our framework successfully yielded replicable conclusions across different training strategies and various clinical datasets. Our findings could thus pave the way for the development of biomarker trustworthiness and reliability assessment methods for computer-aided diagnosis and prognosis tasks. RG-Select code is available on GitHub at https://github.com/basiralab/RG-Select.

Original languageEnglish
Pages (from-to)254-265
Number of pages12
JournalNeural Networks
Early online date3 Feb 2022
Publication statusPublished - Apr 2022


  • Brain biomarkers
  • Brain connectivity multigraphs
  • Graph neural networks
  • Reproducibility


Dive into the research topics of 'Quantifying the reproducibility of graph neural networks using multigraph data representation'. Together they form a unique fingerprint.

Cite this