Besides its really impressive and useful data visualization, the igraph package has a lot more to offer. Unfortunately, this short chapter cannot provide a decent introduction to network analysis theory, but I suggest that you skim through the package documentation as it comes with useful, self-explanatory examples and good references.
In short, network analysis provides various ways to compute centrality and density metrics, like we did at the beginning of this chapter, and also to identify bridges and simulate changes in the network; there are really powerful methods to segment the nodes in the network as well.
For example, in the Financial Networks chapter of the Introduction to R for Quantitative Finance book, which I coauthored, we developed R scripts to identify systemically important financial institutions(SIFI) in Hungary based on the transaction-level network data of the interbank lending market. This dataset and network theory help us to model and...