http://dx.doi.org/10.1007/978-3-642-35527-1_62">
 

Document Type

Conference Paper

Department/Unit

Department of Computer Science

Title

The author-topic-community model: A generative model relating authors’ interests and their community structure

Language

English

Abstract

In this paper, we introduce a generative model named Author-Topic-Community (ATC) model which can infer authors' interests and their community structure at the same time based on the contents and citation information of a document corpus. Via the mutual promotion between the author topics and the author community structure introduced in the ATC model, the robustness of the model towards cases with spare citation information can be enhanced. Variational inference is adopted to estimate the model parameters of ATC. We performed evaluation using both synthetic data as well as a real dataset which contains SIGKDD and SIGMOD papers published in 10 years. By constrasting the performance of ATC with some state-of-the-art methods which model authors' interests and their community structure separately, our experimental results show that 1) the ATC model with the inference of the authors' interests and the community structure integrated can improve the accuracy of author topic modeling and that of author community discovery; and 2) more in-depth analysis of the authors' influence can be readily supported. © Springer-Verlag 2012.

Keywords

Community discovery, Graphical model, User modeling

Publication Date

2012

Source Publication Title

Advanced Data Mining and Applications: 8th International Conference, ADMA 2012, Nanjing, China, December 15-18, 2012. Proceedings

Start Page

753

End Page

765

Conference Location

Nanjing, China

Publisher

Springer

ISBN (print)

9783642355264

ISBN (electronic)

9783642355271

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