Interactive exploration of large time-dependent bipartite graphs

Information

• Publication Type: Journal Paper (without talk)
• Workgroup(s)/Project(s):
  • Visual Information Foraging on the Desktop
• Date: April 2020
• DOI: https://doi.org/10.1016/j.cola.2020.100959
• Journal: Journal of Computer Languages
• Volume: 57
• Keywords: Information visualization, Bipartite graphs, Clustering, Time series data, Insight-based evaluation

Abstract

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.

Additional Files and Images

Weblinks

• paper
  Open Access article at ScienceDirect
• Entry in reposiTUm (TU Wien Publication Database)
• DOI: https://doi.org/10.1016/j.cola.2020.100959

BibTeX

@article{waldner-2020-tbg,
  title =      "Interactive exploration of large time-dependent bipartite
               graphs",
  author =     "Manuela Waldner and Daniel Steinb\"{o}ck and Eduard
               Gr\"{o}ller",
  year =       "2020",
  abstract =   "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.",
  month =      apr,
  doi =        "https://doi.org/10.1016/j.cola.2020.100959",
  journal =    "Journal of Computer Languages",
  volume =     "57",
  keywords =   "Information visualization, Bipartite graphs, Clustering,
               Time series data, Insight-based evaluation",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2020/waldner-2020-tbg/",
}