Summary
In this chapter, we covered the practical applications of graph deep learning in social network analysis using PyG. We focused on a hypothetical dataset representing a university student social network, demonstrating how graph-based machine learning can be applied to real-world scenarios.
Together, we achieved two main tasks: node classification for predicting user interests and link prediction for recommending new connections. By following step-by-step instructions, you learned how to create a synthetic dataset, implement GCNs for node classification, and use GAEs for link prediction. We broke down our code into snippets in relation to data preparation, model training, evaluation, and visualization, allowing you to understand the practical aspects of applying graph deep learning techniques to social network data.
In the upcoming chapters, we will explore how graph deep learning is being applied to various domains, starting with natural language processing.
