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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 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
The transformative potential of AI in software engineering: a case study on LeetCode and ChatGPT
(2026)
The recent surge in the field of generative artificial intelligence (GenAI) has the potential to bring about transformative changes across a range of sectors, including software engineering and education. As GenAI tools, such as OpenAI’s ChatGPT, are increasingly utilised in software engineering, it becomes imperative to understand the impact of these technologies on the software product. This study employs a methodological approach, comprising web scraping and data mining from LeetCode, with the objective of comparing the software quality of Python programs produced by LeetCode users with that generated by GPT-4o. In order to gain insight into these matters, this study addresses the question whether GPT-4o produces software of superior quality to that produced by humans. The findings indicate that GPT-4o does not present a considerable impediment to code quality, understandability, or runtime when generating code on a limited scale. Indeed, the generated code even exhibits significantly better values across all the three code quality dimensions in comparison to the user-written code. However, no significantly superior values were observed for the generated code in terms of memory usage in comparison to the user code, which contravened the expectations. Furthermore, it will be demonstrated that GPT-4o encountered challenges in generalising to problems that were not included in the training data set. This contribution presents a first large-scale study comparing generated code with human-written code based on LeetCode platform based on multiple measures including code quality, code understandability, time behaviour and resource utilisation. All data is publicly available for further research.
