Graph Learning in the Real World
In the ever-evolving landscape of data science and machine learning (ML), the transformative power of graph-based learning has emerged as a pivotal force in unraveling the complexities of real-world phenomena. From social networks and transportation systems to biological interactions and e-commerce, a multitude of intricate systems can be abstracted and analyzed through the lens of graphs. This chapter delves into the fascinating realm of graph learning in the real world, where we explore the compelling idea that many intricate real-world problems can be effectively translated into node-, edge-, and graph-level prediction tasks.
Graphs, composed of nodes representing entities and edges capturing relationships between them, provide an intuitive framework for modeling interconnected structures. By harnessing the inherent relationships encoded in graphs, we gain a powerful tool for understanding the dynamics and patterns underlying diverse domains....
