In this chapter, we covered many of the most critical graph statistics, dividing them into three basic categories: network, centrality, and clustering measures. In the first section, we walked through brief overviews of what each of these statistics can provide for analyzing networks in Gephi.

The second section focused on the interpretation of each of these statistics, when and where they are useful, and what sort of numerical output to anticipate in each case. This was followed by examples of actual applications of these statistics using our primary school network graph.

We closed the chapter with multiple examples showing how graph statistics and filtering can be used together for advanced analysis of any network graph. These examples provided step-by-step instructions that can serve as templates for additional statistical filtering.

We'll now move on to Chapter 7, Segmenting and Partitioning a Graph, where we will explore the multiple methods to improve our graph by identifying common node characteristics.