Network analysis literacy, data analysis literacy, and socioinformatics

Abstract

Abstract Complex network analysis is concerned with identifying statistically significant patterns in large and complex networks. A complex network is an abstract model of a complex system; it represents a well-chosen set of entities as nodes and one or more types of relationships between them as edges. Methods from complex network analysis have been used to identify small molecules called miRNAs that are able to stop breast cancer [13], to understand possible privacy breaches [9], or to analyze how humans solve complex problems [10]. As many areas in our globalized world tend to get more interconnected, the methods from complex network analysis became more important: for complex systems with an underlying network structure, the framework of complex network analysis provides the potential to identify central nodes, to describe deviating substructures, and to reveal the interaction between structure and function of complex networks. Based on this potential impact of network analysis on many fields of society, my work concentrates on understanding when to use which kind of network measure to analyze complex networks and where their limits are, a field I call network analysis literacy. This question can easily be generalized to data analysis literacy which can be even more generalized to the influence of our modern IT-systems on the individual, on organizations, and on society at large, which culminates in a new field of study called socioinformatics.