Graph Deep Learning Challenges

As deep learning on graphs has gained significant attention in recent years, researchers and practitioners have encountered numerous challenges that complicate the application of traditional deep learning techniques to graph data.

This chapter aims to give you a comprehensive overview of key challenges faced in graph learning, spanning from fundamental data issues to advanced model architectures and domain-specific problems. We will explore how the unique properties of graphs—such as their irregular structure, variable size, and complex dependencies—pose significant hurdles for conventional machine learning approaches.

By addressing these challenges, we aim to provide you with a solid foundation for understanding the current limitations and future directions of deep learning with respect to graphs. This chapter will serve as a roadmap, highlighting areas that require further investigation and innovation to advance the field of graph...