# Graph Neural Networks

In the previous chapters, we have discussed various kinds of neural architectures, ranging from convolutional to recurrent, from attention-based transformers to auto-generated **neural networks** (**NNs**). While these architectures cover a wide range of deep learning problems, they work best with data that exists in a continuous space, typically represented as vectors, or coordinates in a Euclidean space such as **text** (**1D**), **images** (**2D**), and **videos** (**3D**). However, a huge portion of real-world datasets exists in the form of graphs or networks, such as social networks, protein-interaction networks, literature citation networks, and the World Wide Web, to name a few. In this chapter, we’ll learn about **graph neural networks** (**GNNs**) – a class of deep learning models that can natively learn patterns from graph structures.

We’ll first understand the basic concepts related to graphs and GNNs. Then, we’ll explore different types of graph-learning...