After completing this chapter, you should be able to:
List three basic ways that graph data and analytics can improve machine learning
Point out which graph algorithms have proved valuable for unsupervised learning
Extract graph features to enrich your training data for supervised machine learning
Describe how neural networks have been extended to learn on graphs
Provide use cases and examples to illustrate graph-powered machine learning
Choose which types of graph-powered machine learning are right for you
We now begin the third theme of our book: Learn. That is, we’re going to get serious about the core of machine learning: model training. Figure 10-1 shows the stages of a simple machine learning pipeline. In Part 1 of this book, we explored the Connect theme, which fits the first two stages of the pipeline: data acquisition and data preparation. Graph databases make it easy to pull data from multiple sources into one connected database and to perform entity resolution.
Figure 10-1. Machine learning pipeline
In this chapter, we’ll show how graphs enhance the central stages in the pipeline: feature extraction and all-important model training. Features are simply the characteristics or properties of your data entities, like the age of a person or the color of a sweater. Graphs offer a whole new realm of features ...
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