Introduction to Graph and Graph Representation Learning

Graphs are a powerful and widely used mathematical framework for modeling the complex relational data across various applications. This chapter introduces the basic fundamentals of graph theory and Graph Representation Learning (GRL). It provides the details of how the data are represented in a graph. It covers the definition of a graph, its importance, and major graph types. It further explains graph connectivity and neighborhood structures, highlighting their role in representing the learned information. An overview of GRL and its benefits are presented along with neighborhood types. To bridge theory with practice, the chapter presents real-world applications of graphs in traffic prediction, social network analysis, pattern recognition, molecular biology, and chemistry. Real-world case studies demonstrate the practical relevance of GRL in domains. The chapter concludes by emphasizing the foundational role of graphs and GRL in modern data-driven systems.