Introduction
摘要
In the modern era, machine learning has experienced a revolutionary transformation, driven by the rise of graph representation learning and the subsequent evolution of graph neural networks (GNNs). Graph data from different domains, from social networks and molecular structures to recommendation systems and knowledge graphs, have catalyzed this shift. Conventional machine learning methods frequently fall short in capturing the intricate relationships and interdependencies present within these graph data.