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Discovering parametric clusters in social small-world graphs

Published: 13 March 2005 Publication History

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.

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Cited By

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  • (2019)User Evaluation of Group-in-a-Box Variants2019 IEEE Pacific Visualization Symposium (PacificVis)10.1109/PacificVis.2019.00023(127-136)Online publication date: Apr-2019
  • (2014)Group-in-a-Box Meta-Layouts for Topological Clusters and Attribute-Based GroupsComputer Graphics Forum10.1111/cgf.1240033:8(52-68)Online publication date: 1-Dec-2014
  • (2014)Compressed representations for web and social graphsKnowledge and Information Systems10.1007/s10115-013-0648-440:2(279-313)Online publication date: 1-Aug-2014
  • Show More Cited By

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cover image ACM Conferences
SAC '05: Proceedings of the 2005 ACM symposium on Applied computing
March 2005
1814 pages
ISBN:1581139640
DOI:10.1145/1066677
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 13 March 2005

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Author Tags

  1. graph clustering
  2. graph layout
  3. histogram
  4. information visualization
  5. machine learning
  6. self-organizing map
  7. small-world graph
  8. social networks
  9. user interface design

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SAC05
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SAC05: The 2005 ACM Symposium on Applied Computing
March 13 - 17, 2005
New Mexico, Santa Fe

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

View all
  • (2019)User Evaluation of Group-in-a-Box Variants2019 IEEE Pacific Visualization Symposium (PacificVis)10.1109/PacificVis.2019.00023(127-136)Online publication date: Apr-2019
  • (2014)Group-in-a-Box Meta-Layouts for Topological Clusters and Attribute-Based GroupsComputer Graphics Forum10.1111/cgf.1240033:8(52-68)Online publication date: 1-Dec-2014
  • (2014)Compressed representations for web and social graphsKnowledge and Information Systems10.1007/s10115-013-0648-440:2(279-313)Online publication date: 1-Aug-2014
  • (2007)Selecting the Most Influential Nodes in Social Networks2007 International Joint Conference on Neural Networks10.1109/IJCNN.2007.4371333(2397-2402)Online publication date: Aug-2007

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