Now that you are equipped with the fundamental knowledge of graph terminology and modeling, you are ready to learn how to use a graph query language and graph algorithms to perform a social network analysis and uncover hidden or indirect patterns that might be missed with traditional analytical approaches. Chapters 3 and 4 are designed to introduce you to Cypher query language and show you how to use it in exploratory graph analysis. In chapter 5, you will learn how to characterize a network by examining a social graph of Twitter followers. You’ll look at the node degree distribution and use graph algorithms, like weakly connected components and local clustering coefficient algorithms, followed by identifying the most important nodes in the graph with the PageRank algorithm. A significant part of graph analysis involves the transformation of a graph to align with the structure the graph algorithms expect. Therefore, you will learn how to transform an indirect relationship to a direct one in chapter 6, construct and analyze a co-occurrence network in chapter 7, and manually define and extract node role features and use them to produce a nearest neighbor graph in chapter 8.