Information
- Publication Type: Conference Paper
- Workgroup(s)/Project(s):
- Date: October 2018
- Organization: IEEE
- Location: Konstanz, Germany
- Lecturer: Manuela Waldner
- Event: 4th International Symposium on Big Data Visual and Immersive Analytics
- Booktitle: International Symposium on Big Data Visual and Immersive Analytics
- 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
Additional images and videos
teaser:
BiCFlows showing visualization authors and their key words
Additional files
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 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, organization = "IEEE", location = "Konstanz, Germany", event = "4th International Symposium on Big Data Visual and Immersive Analytics", booktitle = "International Symposium on Big Data Visual and Immersive Analytics", keywords = "information visualization, bipartite graphs, biclustering, insight-based evaluation", URL = "https://www.cg.tuwien.ac.at/research/publications/2018/steinboeck-2018-lbg/", }