It's a bit of both but the bulk of the material is focused on hands-on examples.
The first two chapters provide background on graph and graph analytics concepts for those that are new to the ideas. We have 3 chapters that deal with the classic graph algorithm categories: pathfinding, centrality, and communities. In those chapters, we go into some detail on key algorithms and how they work (how they calculate), then overview examples uses, and finally show code for how to run it in Spark / Neo4j. We also have 2 chapters that are examples of workflows to solve problems like recommendations and link prediction.