Abstract
Bipartite graphs appear in various scenes in the real world, and visualizing these graphs helps improve our understanding of network structures. The amount of information that is available to us has increased dramatically in recent years, and it is therefore necessary to develop a drawing technique that corresponds to large-scale graphs. In this paper, we describe drawing methods to make large-scale bipartite graphs easy to read. We propose two techniques: "node contraction drawing," which involves collecting similar nodes and drawing them as one node, and "isosimilarity contour drawing," which puts clusters into an outlined area. We developed interactive user interfaces for the drawing methods and conducted an evaluation experiment to demonstrate the effectiveness of the proposed techniques.
| Original language | English |
|---|---|
| Title of host publication | Knowledge-Based Intelligent Information and Engineering Systems - 12th International Conference, KES 2008, Proceedings |
| Pages | 831-838 |
| Number of pages | 8 |
| Volume | 5178 LNAI |
| Edition | PART 2 |
| DOIs | |
| Publication status | Published - 2008 |
| Externally published | Yes |
| Event | 12th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2008 - Zagreb Duration: 2008 Sep 3 → 2008 Sep 5 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Number | PART 2 |
| Volume | 5178 LNAI |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 12th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2008 |
|---|---|
| City | Zagreb |
| Period | 08/9/3 → 08/9/5 |
Fingerprint
Keywords
- Anchored map
- Bipartite graph
- Clustering
- Graph drawing
- Information visualization
ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
Readable representations for large-scale bipartite graphs. / Sato, Shuji; Misue, Kazuo; Tanaka, Jiro.
Knowledge-Based Intelligent Information and Engineering Systems - 12th International Conference, KES 2008, Proceedings. Vol. 5178 LNAI PART 2. ed. 2008. p. 831-838 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5178 LNAI, No. PART 2).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Readable representations for large-scale bipartite graphs
AU - Sato, Shuji
AU - Misue, Kazuo
AU - Tanaka, Jiro
PY - 2008
Y1 - 2008
N2 - Bipartite graphs appear in various scenes in the real world, and visualizing these graphs helps improve our understanding of network structures. The amount of information that is available to us has increased dramatically in recent years, and it is therefore necessary to develop a drawing technique that corresponds to large-scale graphs. In this paper, we describe drawing methods to make large-scale bipartite graphs easy to read. We propose two techniques: "node contraction drawing," which involves collecting similar nodes and drawing them as one node, and "isosimilarity contour drawing," which puts clusters into an outlined area. We developed interactive user interfaces for the drawing methods and conducted an evaluation experiment to demonstrate the effectiveness of the proposed techniques.
AB - Bipartite graphs appear in various scenes in the real world, and visualizing these graphs helps improve our understanding of network structures. The amount of information that is available to us has increased dramatically in recent years, and it is therefore necessary to develop a drawing technique that corresponds to large-scale graphs. In this paper, we describe drawing methods to make large-scale bipartite graphs easy to read. We propose two techniques: "node contraction drawing," which involves collecting similar nodes and drawing them as one node, and "isosimilarity contour drawing," which puts clusters into an outlined area. We developed interactive user interfaces for the drawing methods and conducted an evaluation experiment to demonstrate the effectiveness of the proposed techniques.
KW - Anchored map
KW - Bipartite graph
KW - Clustering
KW - Graph drawing
KW - Information visualization
UR - http://www.scopus.com/inward/record.url?scp=57749170924&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=57749170924&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-85565-1-103
DO - 10.1007/978-3-540-85565-1-103
M3 - Conference contribution
AN - SCOPUS:57749170924
SN - 3540855645
SN - 9783540855644
VL - 5178 LNAI
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 831
EP - 838
BT - Knowledge-Based Intelligent Information and Engineering Systems - 12th International Conference, KES 2008, Proceedings
ER -