Bayesian Complex Network Community Detection Using Nonparametric Topic Model

Ruimin Zhu, Wenxin Jiang

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

Abstract

Network community detection is an important area of research. In this work, we propose a novel nonparametric probabilistic model for this task. We conduct random walks on the network and apply the Hierarchical Dirichlet Process topic model on the random walk data to explore the community structure of the network. Our work is among the very few endeavors in nonparametric probabilistic modeling in complex networks. Our proposed model is highly flexible. The nonparametric nature allows it to automatically detect the number of communities without prior knowledge. Our model is also quite powerful. It demonstrates significant improvements compared to other models in several experiments.

Original languageEnglish (US)
Title of host publicationComplex Networks and Their Applications VII - Volume 1 Proceedings The 7th International Conference on Complex Networks and their Applications COMPLEX NETWORKS 2018
EditorsRenaud Lambiotte, Luis M. Rocha, Pietro Lió, Hocine Cherifi, Luca Maria Aiello, Chantal Cherifi
PublisherSpringer Verlag
Pages280-291
Number of pages12
ISBN (Print)9783030054106
DOIs
StatePublished - Jan 1 2019
Event7th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2018 - Cambridge, United Kingdom
Duration: Dec 11 2018Dec 13 2018

Publication series

NameStudies in Computational Intelligence
Volume812
ISSN (Print)1860-949X

Other

Other7th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2018
CountryUnited Kingdom
CityCambridge
Period12/11/1812/13/18

Fingerprint

Complex networks
Experiments

Keywords

  • Bayesian modeling
  • Community detection
  • Nonparametric topic model

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Zhu, R., & Jiang, W. (2019). Bayesian Complex Network Community Detection Using Nonparametric Topic Model. In R. Lambiotte, L. M. Rocha, P. Lió, H. Cherifi, L. M. Aiello, & C. Cherifi (Eds.), Complex Networks and Their Applications VII - Volume 1 Proceedings The 7th International Conference on Complex Networks and their Applications COMPLEX NETWORKS 2018 (pp. 280-291). (Studies in Computational Intelligence; Vol. 812). Springer Verlag. https://doi.org/10.1007/978-3-030-05411-3_23
Zhu, Ruimin ; Jiang, Wenxin. / Bayesian Complex Network Community Detection Using Nonparametric Topic Model. Complex Networks and Their Applications VII - Volume 1 Proceedings The 7th International Conference on Complex Networks and their Applications COMPLEX NETWORKS 2018. editor / Renaud Lambiotte ; Luis M. Rocha ; Pietro Lió ; Hocine Cherifi ; Luca Maria Aiello ; Chantal Cherifi. Springer Verlag, 2019. pp. 280-291 (Studies in Computational Intelligence).
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abstract = "Network community detection is an important area of research. In this work, we propose a novel nonparametric probabilistic model for this task. We conduct random walks on the network and apply the Hierarchical Dirichlet Process topic model on the random walk data to explore the community structure of the network. Our work is among the very few endeavors in nonparametric probabilistic modeling in complex networks. Our proposed model is highly flexible. The nonparametric nature allows it to automatically detect the number of communities without prior knowledge. Our model is also quite powerful. It demonstrates significant improvements compared to other models in several experiments.",
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Zhu, R & Jiang, W 2019, Bayesian Complex Network Community Detection Using Nonparametric Topic Model. in R Lambiotte, LM Rocha, P Lió, H Cherifi, LM Aiello & C Cherifi (eds), Complex Networks and Their Applications VII - Volume 1 Proceedings The 7th International Conference on Complex Networks and their Applications COMPLEX NETWORKS 2018. Studies in Computational Intelligence, vol. 812, Springer Verlag, pp. 280-291, 7th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2018, Cambridge, United Kingdom, 12/11/18. https://doi.org/10.1007/978-3-030-05411-3_23

Bayesian Complex Network Community Detection Using Nonparametric Topic Model. / Zhu, Ruimin; Jiang, Wenxin.

Complex Networks and Their Applications VII - Volume 1 Proceedings The 7th International Conference on Complex Networks and their Applications COMPLEX NETWORKS 2018. ed. / Renaud Lambiotte; Luis M. Rocha; Pietro Lió; Hocine Cherifi; Luca Maria Aiello; Chantal Cherifi. Springer Verlag, 2019. p. 280-291 (Studies in Computational Intelligence; Vol. 812).

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

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AB - Network community detection is an important area of research. In this work, we propose a novel nonparametric probabilistic model for this task. We conduct random walks on the network and apply the Hierarchical Dirichlet Process topic model on the random walk data to explore the community structure of the network. Our work is among the very few endeavors in nonparametric probabilistic modeling in complex networks. Our proposed model is highly flexible. The nonparametric nature allows it to automatically detect the number of communities without prior knowledge. Our model is also quite powerful. It demonstrates significant improvements compared to other models in several experiments.

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Zhu R, Jiang W. Bayesian Complex Network Community Detection Using Nonparametric Topic Model. In Lambiotte R, Rocha LM, Lió P, Cherifi H, Aiello LM, Cherifi C, editors, Complex Networks and Their Applications VII - Volume 1 Proceedings The 7th International Conference on Complex Networks and their Applications COMPLEX NETWORKS 2018. Springer Verlag. 2019. p. 280-291. (Studies in Computational Intelligence). https://doi.org/10.1007/978-3-030-05411-3_23