An LDA-based community structure discovery approach for large-scale social networks

Zhang Haizheng, Qiu Baojun, C. Lee Giles, Foley Henry C, Yen John

Research output: Chapter in Book/Report/Conference proceedingConference contribution

78 Citations (Scopus)

Abstract

Community discovery has drawn significant research interests among researchers from many disciplines for its increasing application in multiple, disparate areas, including computer science, biology, social science and so on. This paper describes an LDA(latent Dirichlet Allocation)-based hierarchical Bayesian algorithm, namely SSN-LDA(Simple Social Network LDA). In SSN-LDA, communities are modeled as latent variables in the graphical model and defined as distributions over the social actor space. The advantage of SSN-LDA. is that it only requires topological information as input. This model is evaluated on two research collaborative networks:CiteSeer and NanoSCI. The experimental results demonstrate that this approach is promising for discovering community structures in large-scale networks.

Original languageEnglish (US)
Title of host publicationISI 2007
Subtitle of host publication2007 IEEE Intelligence and Security Informatics
Pages200-207
Number of pages8
StatePublished - Oct 1 2007
EventISI 2007: 2007 IEEE Intelligence and Security Informatics - New Brunswick, NJ, United States
Duration: May 23 2007May 24 2007

Publication series

NameISI 2007: 2007 IEEE Intelligence and Security Informatics

Other

OtherISI 2007: 2007 IEEE Intelligence and Security Informatics
CountryUnited States
CityNew Brunswick, NJ
Period5/23/075/24/07

Fingerprint

Social sciences
Computer science

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Control and Systems Engineering

Cite this

Haizheng, Z., Baojun, Q., Giles, C. L., Henry C, F., & John, Y. (2007). An LDA-based community structure discovery approach for large-scale social networks. In ISI 2007: 2007 IEEE Intelligence and Security Informatics (pp. 200-207). [4258697] (ISI 2007: 2007 IEEE Intelligence and Security Informatics).
Haizheng, Zhang ; Baojun, Qiu ; Giles, C. Lee ; Henry C, Foley ; John, Yen. / An LDA-based community structure discovery approach for large-scale social networks. ISI 2007: 2007 IEEE Intelligence and Security Informatics. 2007. pp. 200-207 (ISI 2007: 2007 IEEE Intelligence and Security Informatics).
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Haizheng, Z, Baojun, Q, Giles, CL, Henry C, F & John, Y 2007, An LDA-based community structure discovery approach for large-scale social networks. in ISI 2007: 2007 IEEE Intelligence and Security Informatics., 4258697, ISI 2007: 2007 IEEE Intelligence and Security Informatics, pp. 200-207, ISI 2007: 2007 IEEE Intelligence and Security Informatics, New Brunswick, NJ, United States, 5/23/07.

An LDA-based community structure discovery approach for large-scale social networks. / Haizheng, Zhang; Baojun, Qiu; Giles, C. Lee; Henry C, Foley; John, Yen.

ISI 2007: 2007 IEEE Intelligence and Security Informatics. 2007. p. 200-207 4258697 (ISI 2007: 2007 IEEE Intelligence and Security Informatics).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - Community discovery has drawn significant research interests among researchers from many disciplines for its increasing application in multiple, disparate areas, including computer science, biology, social science and so on. This paper describes an LDA(latent Dirichlet Allocation)-based hierarchical Bayesian algorithm, namely SSN-LDA(Simple Social Network LDA). In SSN-LDA, communities are modeled as latent variables in the graphical model and defined as distributions over the social actor space. The advantage of SSN-LDA. is that it only requires topological information as input. This model is evaluated on two research collaborative networks:CiteSeer and NanoSCI. The experimental results demonstrate that this approach is promising for discovering community structures in large-scale networks.

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Haizheng Z, Baojun Q, Giles CL, Henry C F, John Y. An LDA-based community structure discovery approach for large-scale social networks. In ISI 2007: 2007 IEEE Intelligence and Security Informatics. 2007. p. 200-207. 4258697. (ISI 2007: 2007 IEEE Intelligence and Security Informatics).