HSN-PAM: Finding hierarchical probabilistic groups from large-scale networks

Haizheng Zhang; Wei Li; Xuerui Wang; C. Lee Giles; Henry C. Foley; John Yen

doi:10.1109/ICDMW.2007.115

HSN-PAM: Finding hierarchical probabilistic groups from large-scale networks

Haizheng Zhang, Wei Li, Xuerui Wang, C. Lee Giles, Henry C. Foley, John Yen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

12 Scopus citations

Abstract

Real-world social networks are often hierarchical, reflecting the fact that some communities are composed of a few smaller, sub-communities. This paper describes a hierarchical Bayesian model based scheme, namely HSNPAM (Hierarchical Social Network-Pachinko Allocation Model), for discovering probabilistic, hierarchical communities in social networks. This scheme is powered by a previously developed hierarchical Bayesian model. In this scheme, communities are classified into two categories: super-communities and regular-communities. Two different network encoding approaches are explored to evaluate this scheme on research collaborative networks, including CiteSeer and NanoSCI. The experimental results demonstrate that HSN-PAM is effective for discovering hierarchical community structures in large-scale social networks.

Original language	English (US)
Title of host publication	ICDM Workshops 2007 - Proceedings of the 17th IEEE International Conference on Data Mining Workshops
Pages	27-32
Number of pages	6
DOIs	https://doi.org/10.1109/ICDMW.2007.115
State	Published - 2007
Event	17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007 - Omaha, NE, United States Duration: Oct 28 2007 → Oct 31 2007

Publication series

Name	Proceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)	1550-4786

Other

Other	17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007
Country/Territory	United States
City	Omaha, NE
Period	10/28/07 → 10/31/07

All Science Journal Classification (ASJC) codes

General Engineering

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10.1109/ICDMW.2007.115

Cite this

Zhang, H., Li, W., Wang, X., Giles, C. L., Foley, H. C., & Yen, J. (2007). HSN-PAM: Finding hierarchical probabilistic groups from large-scale networks. In ICDM Workshops 2007 - Proceedings of the 17th IEEE International Conference on Data Mining Workshops (pp. 27-32). Article 4476642 (Proceedings - IEEE International Conference on Data Mining, ICDM). https://doi.org/10.1109/ICDMW.2007.115

@inproceedings{0aac609ad444412684c5d2f214bbd78f,

title = "HSN-PAM: Finding hierarchical probabilistic groups from large-scale networks",

abstract = "Real-world social networks are often hierarchical, reflecting the fact that some communities are composed of a few smaller, sub-communities. This paper describes a hierarchical Bayesian model based scheme, namely HSNPAM (Hierarchical Social Network-Pachinko Allocation Model), for discovering probabilistic, hierarchical communities in social networks. This scheme is powered by a previously developed hierarchical Bayesian model. In this scheme, communities are classified into two categories: super-communities and regular-communities. Two different network encoding approaches are explored to evaluate this scheme on research collaborative networks, including CiteSeer and NanoSCI. The experimental results demonstrate that HSN-PAM is effective for discovering hierarchical community structures in large-scale social networks.",

author = "Haizheng Zhang and Wei Li and Xuerui Wang and Giles, {C. Lee} and Foley, {Henry C.} and John Yen",

year = "2007",

doi = "10.1109/ICDMW.2007.115",

language = "English (US)",

isbn = "0769530192",

series = "Proceedings - IEEE International Conference on Data Mining, ICDM",

pages = "27--32",

booktitle = "ICDM Workshops 2007 - Proceedings of the 17th IEEE International Conference on Data Mining Workshops",

note = "17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007 ; Conference date: 28-10-2007 Through 31-10-2007",

}

Zhang, H, Li, W, Wang, X, Giles, CL, Foley, HC & Yen, J 2007, HSN-PAM: Finding hierarchical probabilistic groups from large-scale networks. in ICDM Workshops 2007 - Proceedings of the 17th IEEE International Conference on Data Mining Workshops., 4476642, Proceedings - IEEE International Conference on Data Mining, ICDM, pp. 27-32, 17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007, Omaha, NE, United States, 10/28/07. https://doi.org/10.1109/ICDMW.2007.115

HSN-PAM: Finding hierarchical probabilistic groups from large-scale networks. / Zhang, Haizheng; Li, Wei; Wang, Xuerui et al.
ICDM Workshops 2007 - Proceedings of the 17th IEEE International Conference on Data Mining Workshops. 2007. p. 27-32 4476642 (Proceedings - IEEE International Conference on Data Mining, ICDM).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - HSN-PAM

T2 - 17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007

AU - Zhang, Haizheng

AU - Li, Wei

AU - Wang, Xuerui

AU - Giles, C. Lee

AU - Foley, Henry C.

AU - Yen, John

PY - 2007

Y1 - 2007

N2 - Real-world social networks are often hierarchical, reflecting the fact that some communities are composed of a few smaller, sub-communities. This paper describes a hierarchical Bayesian model based scheme, namely HSNPAM (Hierarchical Social Network-Pachinko Allocation Model), for discovering probabilistic, hierarchical communities in social networks. This scheme is powered by a previously developed hierarchical Bayesian model. In this scheme, communities are classified into two categories: super-communities and regular-communities. Two different network encoding approaches are explored to evaluate this scheme on research collaborative networks, including CiteSeer and NanoSCI. The experimental results demonstrate that HSN-PAM is effective for discovering hierarchical community structures in large-scale social networks.

AB - Real-world social networks are often hierarchical, reflecting the fact that some communities are composed of a few smaller, sub-communities. This paper describes a hierarchical Bayesian model based scheme, namely HSNPAM (Hierarchical Social Network-Pachinko Allocation Model), for discovering probabilistic, hierarchical communities in social networks. This scheme is powered by a previously developed hierarchical Bayesian model. In this scheme, communities are classified into two categories: super-communities and regular-communities. Two different network encoding approaches are explored to evaluate this scheme on research collaborative networks, including CiteSeer and NanoSCI. The experimental results demonstrate that HSN-PAM is effective for discovering hierarchical community structures in large-scale social networks.

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M3 - Conference contribution

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T3 - Proceedings - IEEE International Conference on Data Mining, ICDM

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BT - ICDM Workshops 2007 - Proceedings of the 17th IEEE International Conference on Data Mining Workshops

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ER -

HSN-PAM: Finding hierarchical probabilistic groups from large-scale networks

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