Discovering social groups without having relational data

John Salerno; Zhongfei Zhang; Ronny Lewin; Michael Decker

doi:10.1117/12.542920

Discovering social groups without having relational data

John Salerno
, Zhongfei Zhang
, Ronny Lewin
, Michael Decker

School of Computing

Binghamton University

Air Force Research Laboratory
State University of New York Binghamton University

Research output: Contribution to journal › Conference article › peer-review

Abstract

Who is associated with whom? Who communicates with whom? When two or more individuals get together is there an intended purpose? Who are the leaders/important individuals of the group? What is the organizational structure of the group? These are just a few of the questions that are covered under the topic of social network analysis. Data mining, specifically community generation, attempts to automatically discover and learn these social models. In this paper we present one class of problems which we have called the uni-party data community generation paradigm. We discuss various applications, a methodology and results from two problem domains.

Original language	English
Pages (from-to)	33-40
Number of pages	8
Journal	Proceedings of SPIE - The International Society for Optical Engineering
Volume	5433
DOIs	https://doi.org/10.1117/12.542920
State	Published - 2004
Event	Data Mining and Knowledge Discovery: Theory, Tools, and Technology VI - Orlando, FL, United States Duration: Apr 12 2004 → Apr 13 2004

Keywords

Clustering
Community Generation
Data Mining
EM Algorithm
Graphs

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10.1117/12.542920

Cite this

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title = "Discovering social groups without having relational data",

abstract = "Who is associated with whom? Who communicates with whom? When two or more individuals get together is there an intended purpose? Who are the leaders/important individuals of the group? What is the organizational structure of the group? These are just a few of the questions that are covered under the topic of social network analysis. Data mining, specifically community generation, attempts to automatically discover and learn these social models. In this paper we present one class of problems which we have called the uni-party data community generation paradigm. We discuss various applications, a methodology and results from two problem domains.",

keywords = "Clustering, Community Generation, Data Mining, EM Algorithm, Graphs",

author = "John Salerno and Zhongfei Zhang and Ronny Lewin and Michael Decker",

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pages = "33--40",

journal = "Proceedings of SPIE - The International Society for Optical Engineering",

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TY - JOUR

T1 - Discovering social groups without having relational data

AU - Salerno, John

AU - Zhang, Zhongfei

AU - Lewin, Ronny

AU - Decker, Michael

PY - 2004

Y1 - 2004

N2 - Who is associated with whom? Who communicates with whom? When two or more individuals get together is there an intended purpose? Who are the leaders/important individuals of the group? What is the organizational structure of the group? These are just a few of the questions that are covered under the topic of social network analysis. Data mining, specifically community generation, attempts to automatically discover and learn these social models. In this paper we present one class of problems which we have called the uni-party data community generation paradigm. We discuss various applications, a methodology and results from two problem domains.

AB - Who is associated with whom? Who communicates with whom? When two or more individuals get together is there an intended purpose? Who are the leaders/important individuals of the group? What is the organizational structure of the group? These are just a few of the questions that are covered under the topic of social network analysis. Data mining, specifically community generation, attempts to automatically discover and learn these social models. In this paper we present one class of problems which we have called the uni-party data community generation paradigm. We discuss various applications, a methodology and results from two problem domains.

KW - Clustering

KW - Community Generation

KW - Data Mining

KW - EM Algorithm

KW - Graphs

UR - https://www.scopus.com/pages/publications/3543135903

U2 - 10.1117/12.542920

DO - 10.1117/12.542920

M3 - Conference article

SN - 0277-786X

VL - 5433

SP - 33

EP - 40

JO - Proceedings of SPIE - The International Society for Optical Engineering

JF - Proceedings of SPIE - The International Society for Optical Engineering

T2 - Data Mining and Knowledge Discovery: Theory, Tools, and Technology VI

Y2 - 12 April 2004 through 13 April 2004

ER -

Discovering social groups without having relational data

Abstract

Keywords

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