Conference proceeding
A Bayesian Framework for Community Detection Integrating Content and Link
UAI2009
Conference on uncertainty in Artificial Intelligence (Montea, Quebec, 06/18/2009 - 06/21/2009)
05/09/2012
Abstract
This paper addresses the problem of community detection in networked data that combines link and content analysis. Most existing work combines link and content information by a generative model. There are two major shortcomings with the existing approaches. First, they assume that the probability of creating a link between two nodes is determined only by the community memberships of the nodes; however other factors (e.g. popularity) could also affect the link pattern. Second, they use generative models to model the content of individual nodes, whereas these generative models are vulnerable to the content attributes that are irrelevant to communities. We propose a Bayesian framework for combining link and content information for community detection that explicitly addresses these shortcomings. A new link model is presented that introduces a random variable to capture the node popularity when deciding the link between two nodes; a discriminative model is used to determine the community membership of a node by its content. An approximate inference algorithm is presented for efficient Bayesian inference. Our empirical study shows that the proposed framework outperforms several state-of-the-art approaches in combining link and content information for community detection.
Details
- Title: Subtitle
- A Bayesian Framework for Community Detection Integrating Content and Link
- Creators
- Tianbao YangRong JinYun ChiShenghuo Zhu
- Resource Type
- Conference proceeding
- Publication Details
- UAI2009
- Conference
- Conference on uncertainty in Artificial Intelligence (Montea, Quebec, 06/18/2009 - 06/21/2009)
- Language
- English
- Date published
- 05/09/2012
- Academic Unit
- Computer Science
- Record Identifier
- 9984259484502771
Metrics
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