Document Type

Journal Article

Department/Unit

Department of Computer Science

Title

The author-topic-community model for author interest profiling and community discovery

Language

English

Abstract

In this paper, we propose a generative model named the author-topic-community (ATC) model for representing a corpus of linked documents. The ATC model allows each author to be associated with a topic distribution and a community distribution as its model parameters. A learning algorithm based on variational inference is derived for the model parameter estimation where the two distributions are essentially reinforcing each other during the estimation. We compare the performance of the ATC model with two related generative models using first synthetic data sets and then real data sets, which include a research community data set, a blog data set, a news-sharing data set, and a microblogging data set. The empirical results obtained confirm that the proposed ATC model outperforms the existing models for tasks such as author interest profiling and author community discovery. We also demonstrate how the inferred ATC model can be used to characterize the roles of users/authors in online communities.

Keywords

Graphical models, Author community discovery, Author interest profiling, Variational inference

Publication Date

8-2015

Source Publication Title

Knowledge and Information Systems

Volume

44

Issue

2

Start Page

359

End Page

383

Publisher

Springer Verlag

Peer Reviewed

1

Funder

C. Li’s research is supported in part by NSFC under Grant No. 61370213, National Key Technology R&D Program No. 2012BAH10F03, 2013BAH17F00, Shenzhen Strategic Emerging Industries Program under Grant No. JCYJ20120613150552967 and Science and Technology Development of Shandong Province Nos. 2010GZX20126, 2010GGX10116.

DOI

10.1007/s10115-014-0764-9

Link to Publisher's Edition

http://dx.doi.org/10.1007/s10115-014-0764-9

ISSN (print)

02191377

ISSN (electronic)

02193116

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