A Bayesian Framework for Community Detection Integrating Content and Link

Tianbao Yang, Rong Jin, Yun Chi, Shenghuo Zhu
Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, PMLR R7:615-622, 2009.

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-theart approaches in combining link and content information for community detection.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR7-yang09a, title = {A {B}ayesian Framework for Community Detection Integrating Content and Link}, author = {Yang, Tianbao and Jin, Rong and Chi, Yun and Zhu, Shenghuo}, booktitle = {Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence}, pages = {615--622}, year = {2009}, editor = {Bilmes, Jeff and Ng, Andrew Y.}, volume = {R7}, series = {Proceedings of Machine Learning Research}, month = {18--21 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r7/main/assets/yang09a/yang09a.pdf}, url = {https://proceedings.mlr.press/r7/yang09a.html}, 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-theart approaches in combining link and content information for community detection.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T A Bayesian Framework for Community Detection Integrating Content and Link %A Tianbao Yang %A Rong Jin %A Yun Chi %A Shenghuo Zhu %B Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2009 %E Jeff Bilmes %E Andrew Y. Ng %F pmlr-vR7-yang09a %I PMLR %P 615--622 %U https://proceedings.mlr.press/r7/yang09a.html %V R7 %X 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-theart approaches in combining link and content information for community detection. %Z Reissued by PMLR on 04 October 2026.
APA
Yang, T., Jin, R., Chi, Y. & Zhu, S.. (2009). A Bayesian Framework for Community Detection Integrating Content and Link. Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R7:615-622 Available from https://proceedings.mlr.press/r7/yang09a.html. Reissued by PMLR on 04 October 2026.

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