Latent business networks mining: A probabilistic generative model

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

Department of Information Systems

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

5 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. © 2012 IEEE.

Original language	English
Title of host publication	Proceedings - 2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012
Pages	558-562
DOIs	https://doi.org/10.1109/WI-IAT.2012.195
Publication status	Published - 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
Place	China
City	Macau
Period	4/12/12 → 7/12/12

Research Keywords

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

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10.1109/WI-IAT.2012.195

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@inproceedings{4712d47a16b0407ebb643df8387eac0a,

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. {\textcopyright} 2012 IEEE.",

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 Li, {Wenjie Maggie}",

year = "2012",

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

language = "English",

isbn = "9780769548807",

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",

}

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: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Latent business networks mining

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

AU - Zhang, Wenping

AU - Lau, Raymond Y.K.

AU - Xia, Yunqing

AU - Li, Chunping

AU - Li, Wenjie Maggie

PY - 2012

Y1 - 2012

N2 - 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. © 2012 IEEE.

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. © 2012 IEEE.

KW - Business Networks Mining

KW - Latent Dirichlet Allocation

KW - Semi-supervised Machine Learning

KW - Web Intelligence

UR - http://www.scopus.com/inward/record.url?scp=84878455551&partnerID=8YFLogxK

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84878455551&origin=recordpage

U2 - 10.1109/WI-IAT.2012.195

DO - 10.1109/WI-IAT.2012.195

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9780769548807

SP - 558

EP - 562

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

Y2 - 4 December 2012 through 7 December 2012

ER -

Latent business networks mining: A probabilistic generative model

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Research Keywords

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