Information
- Publication Type: Conference Paper
- Workgroup(s)/Project(s):
- 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
Additional images and videos
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 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/",
}