Information Flow in Graph Neural Networks: A Clinical Triage Use Case
Víctor Valls Mykhaylo Zayats Alessandra Pascale
Graph Neural Networks (GNNs) have gained popularity in healthcare and other
domains due to their ability to process multi-modal and multi-relational
graphs. However, efficient training of GNNs remains challenging, with several
open research questions. In this paper, we investigate how the flow of
embedding information within GNNs affects the prediction of links in Knowledge
Graphs (KGs). Specifically, we propose a mathematical model that decouples the
GNN connectivity from the connectivity of the graph data and evaluate the
performance of GNNs in a clinical triage use case. Our results demonstrate that
incorporating domain knowledge into the GNN connectivity leads to better
performance than using the same connectivity as the KG or allowing
unconstrained embedding propagation. Moreover, we show that negative edges play
a crucial role in achieving good predictions, and that using too many GNN
layers can degrade performance.