Graph Deep Learning in Practice

Having explored the theoretical aspects of graph deep learning in the previous chapters, now is a good time to get our hands dirty by diving into its practical applications.

Social networks have become an integral part of our digital lives, generating vast amounts of data that can provide valuable insights into human behavior, relationships, and social dynamics. Graph deep learning offers powerful tools to analyze and extract meaningful information from these complex networks. In this chapter, we will explore practical applications of graph deep learning techniques to social network analysis using PyTorch Geometric (PyG).

Here, we will focus on a hypothetical dataset representing a social network of university students. This example will demonstrate how graph-based machine learning can be applied to real-world scenarios, such as predicting user interests, recommending new connections, and identifying community structures.

Our dataset consists...