Discovering parametric clusters in social small-world graphs

Jonathan McPherson; Kwan-Liu Ma; Michael Ogawa

doi:10.1145/1066677.1066954

Discovering parametric clusters in social small-world graphs

Jonathan McPherson, Kwan-Liu Ma, Michael Ogawa

Computer Science

Research output: Contribution to conference › Paper › peer-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 language	English (US)
Pages	1231-1238
Number of pages	8
DOIs	https://doi.org/10.1145/1066677.1066954
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

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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10.1145/1066677.1066954

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keywords = "Graph clustering, Graph layout, Histogram, Information visualization, Machine learning, Self-organizing map, Smallworld graph, Social networks, User interface design",

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doi = "10.1145/1066677.1066954",

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AU - Ma, Kwan-Liu

AU - Ogawa, Michael

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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

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EP - 1238

T2 - 20th Annual ACM Symposium on Applied Computing

Y2 - 13 March 2005 through 17 March 2005

ER -

Discovering parametric clusters in social small-world graphs

Abstract

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Keywords

ASJC Scopus subject areas

Access to Document

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