Discovering parametric clusters in social small-world graphs

Jonathan McPherson, Kwan-Liu Ma, Michael Ogawa

Research output: Contribution to conferencePaperpeer-review

8 Scopus citations

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 languageEnglish (US)
Pages1231-1238
Number of pages8
DOIs
StatePublished - Dec 1 2005
Event20th Annual ACM Symposium on Applied Computing - Santa Fe, NM, United States
Duration: Mar 13 2005Mar 17 2005

Other

Other20th Annual ACM Symposium on Applied Computing
CountryUnited States
CitySanta Fe, NM
Period3/13/053/17/05

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

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