Waldner, M., Steinböck, D., & Gröller, E. (2020). Interactive exploration of large time-dependent bipartite graphs. Journal of Computer Languages, 57(100959), 100959. https://doi.org/10.1016/j.cola.2020.100959
Software; Human-Computer Interaction; Clustering; Computer Networks and Communications; Information visualization; Bipartite graphs; Time series data; Insight-based evaluation
Bipartite graphs are typically visualized using linked lists or matrices, but these visualizations neither scale well nor do they convey temporal development. We present a new interactive exploration interface for large, time-dependent bipartite graphs. We use two clustering techniques to build a hierarchical aggregation supporting different exploration strategies. Aggregated nodes and edges are visualized as linked lists with nested time series. We demonstrate two use cases: finding advertising expenses of public authorities following similar temporal patterns and comparing author-keyword co-occurrences across time. Through a user study, we show that linked lists with hierarchical aggregation lead to more insights than without.
Visual Computing and Human-Centered Technology: 100%