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| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Interactive Visualization of Scientific Collaboration Networks based on Graph Neural Networks |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2023 |
| Abstract Text | Collaborative research is becoming increasingly more important and is associated with higher productivity and producing high-quality research output. It serves as a key mechanism for knowledge diffusion within a research community. In this thesis collaborations in computer science denoted by co-authorships in research publications are analyzed and visualized to uncover the social interactions and relationships between authors, institutions and countries. Bibliographic data from DBLP and detailed author information from CSRankings are collected to create a large-scale collaboration network that includes 76’546 publications from 127 conferences, 148’379 collaborations, and 14’555 authors from 597 institutions located across 55 countries. An exploratory data analysis is conducted, and the network is visualized using the advancements in deep learning on graphs with Graph Convolutional Networks. The publicly available CSCollab tool is introduced, which allows filtering the network based on the geographical scope, the research areas and the year of publication. It provides a visualization of the network on an interactive geographical map, an interactive graph visualization, various visualizations of analytics and statistics of the networks, and features to explore the underlying data of the collaborations. |
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