@inproceedings{09a39d351ac341229b43558aa16c43f7,
title = "The author-topic-community model: A generative model relating authors' interests and their community structure",
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.",
keywords = "Community discovery, Graphical model, User modeling",
author = "Chunshan Li and CHEUNG, {Kwok Wai} and Yunming Ye and Xiaofeng Zhang",
note = "Copyright: Copyright 2013 Elsevier B.V., All rights reserved.; 8th International Conference on Advanced Data Mining and Applications, ADMA 2012 ; Conference date: 15-12-2012 Through 18-12-2012",
year = "2012",
doi = "10.1007/978-3-642-35527-1_62",
language = "English",
isbn = "9783642355264",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "753--765",
booktitle = "Advanced Data Mining and Applications - 8th International Conference, ADMA 2012, Proceedings",
}