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.

翻译：图神经网络（GNNs）因其处理多模态和多关系图的能力而在医疗保健及其他领域受到广泛关注。然而，GNN的高效训练仍面临挑战，存在若干待解决的开放研究问题。本文探究GNN中嵌入信息流如何影响知识图谱（KGs）中链接的预测。具体而言，我们提出一个将GNN连接性与图数据连接性解耦的数学模型，并在临床分诊用例中评估GNN的性能。结果表明，在GNN连接性中融入领域知识比使用与KG相同的连接性或允许无约束的嵌入传播能带来更优性能。此外，我们证明负边在实现良好预测中起关键作用，且使用过多GNN层可能会降低性能。