Getting Started with Graph Learning
Welcome to the first chapter of our journey into the world of graph neural networks (GNNs). In this chapter, we will delve into the foundations of GNNs and understand why they are crucial tools in modern data analysis and machine learning. To that end, we will answer three essential questions that will provide us with a comprehensive understanding of GNNs.
First, we will explore the significance of graphs as a representation of data, and why they are widely used in various domains such as computer science, biology, and finance. Next, we will delve into the importance of graph learning, where we will understand the different applications of graph learning and the different families of graph learning techniques. Finally, we will focus on the GNN family, highlighting its unique features, performance, and how it stands out compared to other methods.
By the end of this chapter, you will have a clear understanding of why GNNs are important and how they can be used to solve real-world problems. You will also be equipped with the knowledge and skills you need to dive deeper into more advanced topics. So, let’s get started!
In this chapter, we will cover the following main topics:
- Why graphs?
- Why graph learning?
- Why graph neural networks?
