ViNCent 2.0: En webbaserad implementering för visualisering och analys av multivariata och dynamiska nätverk

Network visualization plays a crucial role in understanding complex relationships across different domains, from social interactions to biological networks. However, as networks grow in size and complexity, traditional visualization approaches struggle to support the simultaneous analysis of multiple measures and temporal evolution. This thesis addresses these challenges by reimplementing and extending ViNCent, a specialized tool for centrality focused network analysis, as a modern web-based system built on a client-server architecture with a TypeScript/React/D3.js frontend and Python/FastAPI/NetworkX backend, supporting multivariate and temporal network exploration and analysis. ViNCent 2.0 preserves the original system's core analytical capabilities while introducing significant extensions. The circular diagram view extends the original centrality focused encoding to support arbitrary node attributes alongside centrality measures, enabling simultaneous comparison of multiple centrality metrics and domain-specific metadata within a unified visual representation. For temporal networks, the system implements a discrete snapshot model with foresighted layouts, chronological node ordering and explicit change indicators to support temporal exploration while maintaining spatial consistency to reduce cognitive load during navigation. Evaluation through a formative user study and structured use case scenario demonstrates that ViNCent 2.0 supports exploratory analysis of multivariate and temporal networks. The user study identified concrete usability improvements that informed design refinements, while the Delhi Metro network use case illustrates how the system enables analysts to track network evolution, identify structural hubs, and relate topological importance to domain specific metadata through coordinated views and interactive filtering. However, to what extent the system supports effective analysis for domain experts remains an open question left for future work. The work contributes a functional web-based platform for network visualization that addresses the specific challenges of comparing multiple measures simultaneously and exploring temporal dynamics. By modernizing ViNCent's centrality focused approach and extending it to support broader analytical requirements, this thesis demonstrates how specialized visualization techniques can be adapted to contemporary web technologies while expanding their applicability to increasingly complex network analysis tasks.