Temporal Graph Machine Learning
In the ever-evolving landscape of data science and machine learning, the study of temporal graphs has emerged as a crucial field with widespread applications. Temporal graphs provide a dynamic representation of relationships and interactions between entities over time, offering a more realistic and nuanced perspective than traditional static graphs.
This chapter explores the fundamental concepts of temporal graphs, delving into their definitions, properties, and common applications in various domains. We will explore the definition of dynamic graphs and why they are needed. We will see common problems that can be modeled in the framework of dynamic graphs and we will explore several machine learning algorithms that have been developed for solving such problems, including temporal graph neural networks.
The following topics will be covered in this chapter:
- The definition of dynamic graphs
- Common problems that can be modeled with...
