11 What’s Next: Graph Analytics, Machine Learning, Resources
This chapter covers
- Introducing common graph analytics algorithms, for pathfinding, centrality, and community detection.
- Introducing to the role of graphs in machine learning.
- Helpful resources for graph theory, graph databases, and graph algorithms
Great, you’ve made it to the final chapter. It’s been a journey as we switched from thinking about problems from a relational, entity-first mindset to a graph, entity-plus-relationships mindset. Even though this is the end of the book, the next phase of your journey with graphs is just beginning. So what’s next, where do you go from here? This chapter will answer these questions by providing an overview of common paths people pursue in extending their knowledge of graphs.
Graph analytics and machine learning are two of the most common areas where exploration of graphs might take you next. This chapter will introduce these two areas and provide you with just enough information to decide if you want to explore these areas further.
We’ll start with a high-level look at graph analytics and some of the unique insights that these algorithms can derive from data. We’ll provide a broad overview of the graph analytics space so that you will have some understanding of what is available when you start analyzing your graph data.