Latent business networks mining: A probabilistic generative model

Wenping Zhang; Raymond Y.K. Lau; Yunqing Xia; Chunping Li; Wenjie Li

doi:10.1109/WI-IAT.2012.195

Latent business networks mining: A probabilistic generative model

Wenping Zhang, Raymond Y.K. Lau, Yunqing Xia, Chunping Li, Wenjie Li

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

2 Citations (Scopus)

Abstract

Though numerous research has been devoted to social network discovery and analysis, relatively little research has been conducted on business network discovery. The main contribution of our research is the development of a novel probabilistic generative model for latent business networks mining. Our experimental results confirm that the proposed method outperforms the well-known vector space based model by 24% in terms of AUC value.

Original language	English
Title of host publication	Proceedings - 2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012
Pages	558-562
Number of pages	5
DOIs	https://doi.org/10.1109/WI-IAT.2012.195
Publication status	Published - 1 Dec 2012
Event	2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012 - Macau, China Duration: 4 Dec 2012 → 7 Dec 2012

Conference

Conference	2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012
Country/Territory	China
City	Macau
Period	4/12/12 → 7/12/12

Keywords

Business Networks Mining
Latent Dirichlet Allocation
Semi-supervised Machine Learning
Web Intelligence

ASJC Scopus subject areas

Artificial Intelligence
Software

More information

10.1109/WI-IAT.2012.195

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title = "Latent business networks mining: A probabilistic generative model",

abstract = "Though numerous research has been devoted to social network discovery and analysis, relatively little research has been conducted on business network discovery. The main contribution of our research is the development of a novel probabilistic generative model for latent business networks mining. Our experimental results confirm that the proposed method outperforms the well-known vector space based model by 24% in terms of AUC value.",

keywords = "Business Networks Mining, Latent Dirichlet Allocation, Semi-supervised Machine Learning, Web Intelligence",

author = "Wenping Zhang and Lau, {Raymond Y.K.} and Yunqing Xia and Chunping Li and Wenjie Li",

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doi = "10.1109/WI-IAT.2012.195",

language = "English",

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pages = "558--562",

booktitle = "Proceedings - 2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012",

note = "2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012 ; Conference date: 04-12-2012 Through 07-12-2012",

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Latent business networks mining: A probabilistic generative model. / Zhang, Wenping; Lau, Raymond Y.K.; Xia, Yunqing et al.

Proceedings - 2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012. 2012. p. 558-562 6511940.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AU - Zhang, Wenping

AU - Lau, Raymond Y.K.

AU - Xia, Yunqing

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AU - Li, Wenjie

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AB - Though numerous research has been devoted to social network discovery and analysis, relatively little research has been conducted on business network discovery. The main contribution of our research is the development of a novel probabilistic generative model for latent business networks mining. Our experimental results confirm that the proposed method outperforms the well-known vector space based model by 24% in terms of AUC value.

KW - Business Networks Mining

KW - Latent Dirichlet Allocation

KW - Semi-supervised Machine Learning

KW - Web Intelligence

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DO - 10.1109/WI-IAT.2012.195

M3 - Conference article published in proceeding or book

SN - 9780769548807

SP - 558

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BT - Proceedings - 2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012

T2 - 2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012

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ER -

Latent business networks mining: A probabilistic generative model

Abstract

Conference

Keywords

ASJC Scopus subject areas

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