Abstract
We present a strategy for analyzing large, social small-world graphs, such as those formed by human networks. Our approach brings together ideas from a number of different research areas, including graph layout, graph clustering and partitioning, machine learning, and user interface design. It helps users explore the networks and develop insights concerning their members and structure that may be difficult or impossible to discover via traditional means, including existing graph visualization and/or statistical methods.
| Original language | English (US) |
|---|---|
| Pages | 1231-1238 |
| Number of pages | 8 |
| DOIs | |
| State | Published - Dec 1 2005 |
| Event | 20th Annual ACM Symposium on Applied Computing - Santa Fe, NM, United States Duration: Mar 13 2005 → Mar 17 2005 |
Other
| Other | 20th Annual ACM Symposium on Applied Computing |
|---|---|
| Country | United States |
| City | Santa Fe, NM |
| Period | 3/13/05 → 3/17/05 |
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Keywords
- Graph clustering
- Graph layout
- Histogram
- Information visualization
- Machine learning
- Self-organizing map
- Smallworld graph
- Social networks
- User interface design
ASJC Scopus subject areas
- Software
Cite this
Discovering parametric clusters in social small-world graphs. / McPherson, Jonathan; Ma, Kwan-Liu; Ogawa, Michael.
2005. 1231-1238 Paper presented at 20th Annual ACM Symposium on Applied Computing, Santa Fe, NM, United States.Research output: Contribution to conference › Paper
}
TY - CONF
T1 - Discovering parametric clusters in social small-world graphs
AU - McPherson, Jonathan
AU - Ma, Kwan-Liu
AU - Ogawa, Michael
PY - 2005/12/1
Y1 - 2005/12/1
N2 - We present a strategy for analyzing large, social small-world graphs, such as those formed by human networks. Our approach brings together ideas from a number of different research areas, including graph layout, graph clustering and partitioning, machine learning, and user interface design. It helps users explore the networks and develop insights concerning their members and structure that may be difficult or impossible to discover via traditional means, including existing graph visualization and/or statistical methods.
AB - We present a strategy for analyzing large, social small-world graphs, such as those formed by human networks. Our approach brings together ideas from a number of different research areas, including graph layout, graph clustering and partitioning, machine learning, and user interface design. It helps users explore the networks and develop insights concerning their members and structure that may be difficult or impossible to discover via traditional means, including existing graph visualization and/or statistical methods.
KW - Graph clustering
KW - Graph layout
KW - Histogram
KW - Information visualization
KW - Machine learning
KW - Self-organizing map
KW - Smallworld graph
KW - Social networks
KW - User interface design
UR - http://www.scopus.com/inward/record.url?scp=33644608756&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33644608756&partnerID=8YFLogxK
U2 - 10.1145/1066677.1066954
DO - 10.1145/1066677.1066954
M3 - Paper
AN - SCOPUS:33644608756
SP - 1231
EP - 1238
ER -