Abstract
The hierarchical edge bundle (HEB) method generates useful visualizations of dense graphs, such as social networks, but requires a predefined clustering hierarchy, and does not easily benefit from existing straight-line visualization improvements. This paper proposes a new clustering approach that extracts the community structure of a network and organizes it into a hierarchy that is flatter than existing community-based clustering approaches and maps better to HEB visualization. Our method not only discovers communities and generates clusters with better modularization qualities, but also creates a balanced hierarchy that allows HEB visualization of unstructured social networks without predefined hierarchies. Results on several data sets demonstrate that this approach clarifies real-world communication, collaboration and competition network structure and reveals information missed in previous visualizations. We further implemented our techniques into a social network visualization application on facebook.com and let users explore the visualization and community clustering of their own social networks.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 2314-2327 |
| Number of pages | 14 |
| Journal | Computer Graphics Forum |
| Volume | 30 |
| Issue number | 8 |
| DOIs | |
| State | Published - Dec 2011 |
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Keywords
- Betweenness centrality
- Edge bundles
- Network clustering
- Visualization
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
Cite this
Social network clustering and visualization using hierarchical edge bundles. / Jia, Yuntao; Garland, Michael; Hart, John C.
In: Computer Graphics Forum, Vol. 30, No. 8, 12.2011, p. 2314-2327.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Social network clustering and visualization using hierarchical edge bundles
AU - Jia, Yuntao
AU - Garland, Michael
AU - Hart, John C.
PY - 2011/12
Y1 - 2011/12
N2 - The hierarchical edge bundle (HEB) method generates useful visualizations of dense graphs, such as social networks, but requires a predefined clustering hierarchy, and does not easily benefit from existing straight-line visualization improvements. This paper proposes a new clustering approach that extracts the community structure of a network and organizes it into a hierarchy that is flatter than existing community-based clustering approaches and maps better to HEB visualization. Our method not only discovers communities and generates clusters with better modularization qualities, but also creates a balanced hierarchy that allows HEB visualization of unstructured social networks without predefined hierarchies. Results on several data sets demonstrate that this approach clarifies real-world communication, collaboration and competition network structure and reveals information missed in previous visualizations. We further implemented our techniques into a social network visualization application on facebook.com and let users explore the visualization and community clustering of their own social networks.
AB - The hierarchical edge bundle (HEB) method generates useful visualizations of dense graphs, such as social networks, but requires a predefined clustering hierarchy, and does not easily benefit from existing straight-line visualization improvements. This paper proposes a new clustering approach that extracts the community structure of a network and organizes it into a hierarchy that is flatter than existing community-based clustering approaches and maps better to HEB visualization. Our method not only discovers communities and generates clusters with better modularization qualities, but also creates a balanced hierarchy that allows HEB visualization of unstructured social networks without predefined hierarchies. Results on several data sets demonstrate that this approach clarifies real-world communication, collaboration and competition network structure and reveals information missed in previous visualizations. We further implemented our techniques into a social network visualization application on facebook.com and let users explore the visualization and community clustering of their own social networks.
KW - Betweenness centrality
KW - Edge bundles
KW - Network clustering
KW - Visualization
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UR - http://www.scopus.com/inward/citedby.url?scp=84858708895&partnerID=8YFLogxK
U2 - 10.1111/j.1467-8659.2011.02037.x
DO - 10.1111/j.1467-8659.2011.02037.x
M3 - Article
AN - SCOPUS:84858708895
VL - 30
SP - 2314
EP - 2327
JO - Computer Graphics Forum
JF - Computer Graphics Forum
SN - 0167-7055
IS - 8
ER -