Complex networks and data science: Case studies in interdisciplinary research
- This habilitation thesis compiles research on the challenges of complex networks in com-
puter 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 areThis habilitation thesis compiles research on the challenges of complex networks in com-
puter 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…


| Author: | Jens Dörpinghaus |
|---|---|
| URN: | urn:nbn:de:hbz:kob7-25555 |
| DOI: | https://doi.org/10.82549/opus4-2555 |
| Place of publication: | Koblenz |
| Advisor: | Jan Jürjens, Frank Hopfgartner, Antonio Mucherino |
| Document Type: | Habilitation |
| Language: | English |
| Date of Publication (online): | 2025/05/27 |
| Date of first Publication: | 2025/05/27 |
| Publishing Institution: | Universität Koblenz, Universitätsbibliothek |
| Granting Institution: | Universität Koblenz, Fachbereich 4 |
| Date of final exam: | 2025/04/29 |
| Release Date: | 2025/05/27 |
| Page Number: | xii, 431 Seiten |
| Institutes: | Fachbereich 4 / Institut für Informatik |
| Licence (German): | Es gilt das deutsche Urheberrecht: § 53 UrhG |

