2009 CombiningLinkandContentforCommu

• (Yang et al., 2009) ⇒ Tianbao Yang, Rong Jin, Yun Chi, and Shenghuo Zhu. (2009). “Combining Link and Content for Community Detection: A Discriminative Approach.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557120

Subject Headings:

Notes

• Categories and Subject Descriptors: H.2.8 Database Management: Database Applications — Data mining; H.3.3 Information Storage and Retrieval: Information Search and Retrieval — Clustering.
• General Terms: Algorithms, Experimentation, Measurement, Theory

Cited By

• http://scholar.google.com/scholar?q=%22Combining+link+and+content+for+community+detection%3A+a+discriminative+approach%22+2009
• http://portal.acm.org/citation.cfm?doid=1557019.1557120&preflayout=flat#citedby

Quotes

Author Keywords

Discriminative Model, EM Algorithm, Link analysis, Two-Stage Optimization

Abstract

In this paper, we consider the problem of combining link and content analysis for community detection from networked data, such as paper citation networks and World Wide Web. Most existing approaches combine link and content information by a generative model that generates both links and contents via a shared set of community memberships. These generative models have some shortcomings in that they failed to consider additional factors that could affect the community memberships and isolate the contents that are irrelevant to community memberships. To explicitly address these shortcomings, we propose a discriminative model for combining the link and content analysis for community detection. First, we propose a conditional model for link analysis and in the model, we introduce hidden variables to explicitly model the popularity of nodes. Second, to alleviate the impact of irrelevant content attributes, we develop a discriminative model for content analysis. These two models are unified seamlessly via the community memberships. We present efficient algorithms to solve the related optimization problems based on bound optimization and alternating projection. Extensive experiments with benchmark data sets show that the proposed framework significantly outperforms the state-of-the-art approaches for combining link and content analysis for community detection.

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References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 CombiningLinkandContentforCommu	Tianbao Yang Rong Jin Yun Chi Shenghuo Zhu			Combining Link and Content for Community Detection: A Discriminative Approach		KDD-2009 Proceedings		10.1145/1557019.1557120		2009