Dynamic Network Analysis with Centrality Measures

Analyzing large, dynamic network data, such as the Database of Modern Exhibitions, presents significant challenges due to the dataset's scale and complexity, as it tracks a decade of European art exhibitions and encompasses thousands of artists with evolving relationships. Centrality measures help address this complexity by using algorithms to quantify the importance of each node. However, an important next step is to explore how these calculated importance metrics can be transformed into meaningful visual representations to extract insights and identify key actors.To achieve this, we conducted a state-of-the-art literature review to investigate how centrality measures are applied in data visualization, which informed the development of dome-insights, a visual analytics tool that integrates centrality measures into both its visualization and interaction design. Dome-insights serves as a prototype to demonstrate how incorporating centrality measures can enhance the exploration of large, complex networks, facilitating insight discovery and identification of key actors.The tool was assessed by art historians and delivered positive results in both quantitative and qualitative evaluations, demonstrating its ability to uncover meaningful insights. More broadly, the findings highlight the potential of centrality measures in dynamic network analysis, underscoring their role in enhancing visual analytics for complex network data.