Information

• Publication Type: Conference Paper
• Workgroup(s)/Project(s):
  • Visual Information Foraging on the Desktop
• Date: October 2018
• Booktitle: International Symposium on Big Data Visual and Immersive Analytics
• Event: 4th International Symposium on Big Data Visual and Immersive Analytics
• Lecturer: Manuela Waldner
• Location: Konstanz, Germany
• Organization: IEEE
• Keywords: information visualization, bipartite graphs, biclustering, insight-based evaluation

Abstract

Bipartite graphs are typically visualized using linked lists or matrices. However, these classic visualization techniques do not scale well with the number of nodes. Biclustering has been used to aggregate edges, but not to create linked lists with thousands of nodes. In this paper, we present a new casual exploration interface for large, weighted bipartite graphs, which allows for multi-scale exploration through hierarchical aggregation of nodes and edges using biclustering in linked lists. We demonstrate the usefulness of the technique using two data sets: a database of media advertising expenses of public authorities and author-keyword co-occurrences from the IEEE Visualization Publication collection. Through an insight-based study with lay users, we show that the biclustering interface leads to longer exploration times, more insights, and more unexpected findings than a baseline interface using only filtering. However, users also perceive the biclustering interface as more complex.

Additional Files and Images

Weblinks

• BiCFlows online
  BiCFlows online for exploring Austria's media transparency database and the IEEE Visualization paper authors and their key words.

BibTeX

@inproceedings{steinboeck-2018-lbg,
  title =      "Casual Visual Exploration of Large Bipartite Graphs Using
               Hierarchical Aggregation and Filtering",
  author =     "Daniel Steinb\"{o}ck and Meister Eduard Gr\"{o}ller and
               Manuela Waldner",
  year =       "2018",
  abstract =   "Bipartite graphs are typically visualized using linked lists
               or matrices. However, these classic visualization techniques
               do not scale well with the number of nodes. Biclustering has
               been used to aggregate edges, but not to create linked lists
               with thousands of nodes. In this paper, we present a new
               casual exploration interface for large, weighted bipartite
               graphs, which allows for multi-scale exploration through
               hierarchical aggregation of nodes and edges using
               biclustering in linked lists. We demonstrate the usefulness
               of the technique using two data sets: a database of media
               advertising expenses of public authorities and
               author-keyword co-occurrences from the IEEE Visualization
               Publication collection. Through an insight-based study with
               lay users, we show that the biclustering interface leads to
               longer exploration times, more insights, and more unexpected
               findings than a baseline interface using only filtering.
               However, users also perceive the biclustering interface as
               more complex.",
  month =      oct,
  booktitle =  "International Symposium on Big Data Visual and Immersive
               Analytics",
  event =      "4th International Symposium on Big Data Visual and Immersive
               Analytics",
  location =   "Konstanz, Germany",
  organization = "IEEE",
  keywords =   "information visualization, bipartite graphs, biclustering,
               insight-based evaluation",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2018/steinboeck-2018-lbg/",
}