Are Population Graphs Really as Powerful as Believed?

Abstract

Population graphs and their use in combination with graph neural networks (GNNs) have demonstrated promising results for multi-modal medical data integration and improving disease diagnosis and prognosis. Several different methods for constructing these graphs and advanced graph learning techniques have been established to maximise the predictive power of GNNs on population graphs. However, in this work, we raise the question of whether existing methods are really strong enough by showing that simple baseline methods --such as random forests or linear regressions--, perform on par with advanced graph learning models on several population graph datasets for a variety of different clinical applications. We use the commonly used public population graph datasets TADPOLE and ABIDE, a brain age estimation and a cardiac dataset from the UK Biobank, and a real-world in-house COVID dataset. We (a) investigate the impact of different graph construction methods, graph convolutions, and dataset size and complexity on GNN performance and (b) discuss the utility of GNNs for multi-modal data integration in the context of population graphs. Based on our results, we argue towards the need for "better" graph construction methods or innovative applications for population graphs to render them beneficial.