Emerging Applications
Graph deep learning has demonstrated remarkable versatility across a wide array of domains, extending far beyond its well-known applications in natural language processing (NLP), recommendation systems, and computer vision (CV), as we saw in Chapters 8, 9, and 10, respectively. Here, we explore the diverse landscape of applications where graph-based approaches have made significant impacts or show promising potential.
As we delve into these applications, we’ll see how graph deep learning techniques adapt to different contexts, often providing novel solutions to long-standing challenges. In urban planning, for example, these methods have been used to optimize public transportation networks and predict traffic flow, contributing to the development of smarter, more efficient cities. In the realm of materials science, researchers are leveraging graph-based models to predict material properties and design new compounds with specific characteristics, potentially...
