Network Analysis Literacy

Network analysis offers a way to detect and measure meaningful patterns in how different kinds of entities interact, and to connect those patterns to the traits of the entities involved. The approach has been applied to biological and social networks, as well as to complex systems in economics, psychology, geography and many other disciplines. Open-source tools, especially network packages for R, now let researchers in any field run these methods on their own data, but the sheer number of available techniques can be bewildering to newcomers.

This book maps out that territory and explains how to choose a suitable method for a particular project while avoiding common mistakes. It covers the techniques most often used on complex networks, including global network measures, several families of random graph models, centrality indices and network motifs. Its main goal is to build network analysis literacy, meaning the judgment needed to know which method answers which kind of question. To help readers handle the original research literature, it includes a glossary and explains formal notation and mathematical symbols in plain language. Exercises with worked solutions reinforce understanding of formal definitions, programming tasks, and the interpretation of structural measures.