<p>Quantifying the reproducibility of graph neural networks using multigraph data representation & nbsp;</p>

Nebli A., Gharsallaoui M. A., Gurler Z., Rekık I., Alzheimers Dis Neuroimaging Initiative A. D. N. I.

NEURAL NETWORKS, vol.148, pp.254-265, 2022 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 148
  • Publication Date: 2022
  • Doi Number: 10.1016/j.neunet.2022.01.018
  • Journal Name: NEURAL NETWORKS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, BIOSIS, Biotechnology Research Abstracts, Communication Abstracts, Compendex, Computer & Applied Sciences, EMBASE, INSPEC, MEDLINE, Psycinfo, zbMATH
  • Page Numbers: pp.254-265
  • Keywords: Reproducibility, Graph neural networks, Brain connectivity multigraphs, Brain biomarkers, BRAIN CONNECTIVITY, CINGULATE CORTEX
  • Istanbul Technical University Affiliated: Yes


Graph neural networks (GNNs) have witnessed an unprecedented proliferation in tackling several prob-lems 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. (C) 2022 Elsevier Ltd. All rights reserved.