Complex networks and data science : case studies in interdisciplinary research

Abstract

"This habilitation thesis compiles research on the challenges of complex networks in computer science and their applications. It includes case studies on interdisciplinary research in life sciences, computational social sciences, and digital humanities. In the life sciences, knowledge graph approaches are commonly used for clinical and biomedical data. This thesis focuses on context mining, algorithmic challenges, and link prediction. In social sciences network approaches, the goal is to connect social network analysis with ontology driven research on the labor market. Although data sets are frequently available in social sciences, this is not always the case in the humanities. Therefore, when applying complex network approaches such as social network analysis to textual data, hermeneutical and methodological considerations are necessary. Once these considerations are addressed, data science methods such as text mining can be used to construct networks from texts. This thesis presents two case studies on social network analysis, in addition to addressing the challenges of interdisciplinary research on complex networks in computer science. By describing three different domains, it demonstrates the existence of a common toolbox that utilizes methods from data science and graph theory. Consequently, this thesis argues for more interdisciplinary exchange." (Authors' abstract, BIBB-Doku)