Latent Business Networks Mining: A Probabilistic Generative Model

Authors:
Wenping Zhang;Raymond Y. K. Lau;Yunqing Xia;Chunping Li;Wenjie Maggie Li
Affiliations:
-;-;-;-;-
Venue:
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Year:
2012

Citing 13
Cited 0

The small-world phenomenon: an algorithmic perspective

STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems

Machine Learning
Introduction to Modern Information Retrieval

Introduction to Modern Information Retrieval
Latent dirichlet allocation

The Journal of Machine Learning Research
SemRank: ranking complex relationship search results on the semantic web

WWW '05 Proceedings of the 14th international conference on World Wide Web
Ranking Complex Relationships on the Semantic Web

IEEE Internet Computing
LDA-based document models for ad-hoc retrieval

SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Mining business topics in source code using latent dirichlet allocation

ISEC '08 Proceedings of the 1st India software engineering conference
Opinion mining and relationship discovery using CopeOpi opinion analysis system

Journal of the American Society for Information Science and Technology
Novel relationship discovery using opinions mined from the web

AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Discovering company revenue relations from news: A network approach

Decision Support Systems
Expectation-propagation for the generative aspect model

UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Interactive relationship discovery via the semantic web

ESWC'10 Proceedings of the 7th international conference on The Semantic Web: research and Applications - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

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.