Scaling Graph Learning for the Enterprise

Chapter 3. Traditional Machine Learning for Graphs

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In this chapter, we will explore both traditional and non-traditional machine learning approaches applied to graphs. Then, we will dive into traditional graph-based machine learning, building upon the foundational concepts introduced in Chapter 2. Starting, we’ll explore the nuances of graph data representation, transitioning from general methods to a focused case study on the Amazon co-purchasing network. As we navigate through, we’ll uncover the diverse tasks that can be tackled using this dataset.

The heart of our exploration lies in graph feature engineering — a pivotal step that can make or break the performance of machine learning models. Here, we’ll unravel the importance of this process, the challenges encountered, and the different types of features that can be derived from graphs. Our hands-on approach will guide you through feature extraction, culminating in the integration ...