Network Analysis Literacy

This book presents a perspective of network analysis as a tool to find and quantify significant structures in the interaction patterns between different types of  entities. Moreover, network analysis provides the basic means to relate these structures to properties of the entities. It has proven itself to be useful for the analysis of biological and social networks, but also for networks describing complex systems in economy, psychology, geography, and various other fields. Today, network analysis packages in the open-source platform R and other open-source software projects enable scientists from all fields to quickly apply network analytic methods to their data sets. Altogether, these applications offer such a wealth of network analytic methods that it can be overwhelming for someone just entering this field. This book provides a road map through this jungle of network analytic methods, offers advice on how to pick the best method for a given network analytic project, and how to avoid common pitfalls. It introduces the methods which are most often used to analyze complex networks, e.g., different global network measures, types of random graph models, centrality indices, and networks motifs. In addition to introducing these methods, the central focus is on network analysis literacy - the competence to decide when to use which of these methods for which type of question. Furthermore, the book intends to increase the reader's competence to read original literature on network analysis by providing a glossary and intensive translation of formal notation and mathematical symbols in everyday speech. Different aspects of network analysis literacy - understanding formal definitions, programming tasks, or the analysis of structural measures and their interpretation - are deepened in various exercises with provided solutions. This text is an excellent, if not the best starting point for all scientists who want to harness the power of network analysis for their field of expertise.

Katharina A. Zweig has studied biochemistry and bioinformatics at the University Tübingen, Germany (1996-2006). In her doctoral studies, she was concerned with the relation of local structure and global behavior in the evolution of complex networks (2007, University Tübingen, Germany). Her postdoc was done in statistical physics at ELTE University, Hungary (2008-09); after that she lead an independent junior research group at University Heidelberg, Germany (2009-2012). Since 2012, she is a professor for 'Graph Theory and Complex Network Analysis' at TU Kaiserslautern, Germany. She is a Junior Fellow of the German Society of Computer Science and was elected as one of 39 Digital Thinkers ('Digitale Köpfe') in 2014 in Germany. Since she started to work on the use of centrality indices in network analysis in 2003, network analysis literacy has been her main concern. Currently, her research is focused on the more general question of how to make algorithms accountable, i.e., how to make sure that an algorithm's numerical answer matches the intuition behind the problem modeled by it. In 2016, she co-founded Algorithm Watch, a non-profit organisation to hold algorithms accountable and to consult society, politics, industry and public institutions about all questions concerning algorithm accountability.