The Importance of Model Inspection for Better Understanding Performance Characteristics of Graph Neural Networks

arXiv:2405.01270v1 Announce Type: new
Abstract: This study highlights the importance of conducting comprehensive model inspection as part of comparative performance analyses. Here, we investigate the effect of modelling choices on the feature learning characteristics of graph neural networks applied to a brain shape classification task. Specifically, we analyse the effect of using parameter-efficient, shared graph convolutional submodels compared to structure-specific, non-shared submodels. Further, we assess the effect of mesh registration as part of the data harmonisation pipeline. We find substantial …

abstract arxiv brain classification cs.lg feature graph graph neural networks highlights importance modelling networks neural networks part performance study type understanding