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) |
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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 |
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Country/Territory | United States |
City | Santa Fe, NM |
Period | 3/13/05 → 3/17/05 |
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