2014 CommunityDetectioninGraphsthrou

• (Duan et al., 2014) ⇒ Lian Duan, Willian Nick Street, Yanchi Liu, and Haibing Lu. (2014). “Community Detection in Graphs through Correlation.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623629

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222014%22+Community+Detection+in+Graphs+through+Correlation
• http://dl.acm.org/citation.cfm?id=2623330.2623629&preflayout=flat#citedby

Quotes

Author Keywords

• Community detection; correlation analysis; data mining; leverage; likelihood ratio; modularity

Abstract

Community detection is an important task for social networks, which helps us understand the functional modules on the whole network. Among different community detection methods based on graph structures, modularity-based methods are very popular recently, but suffer a well-known resolution limit problem. This paper connects modularity-based methods with correlation analysis by subtly reformatting their math formulas and investigates how to fully make use of correlation analysis to change the objective function of modularity-based methods, which provides a more natural and effective way to solve the resolution limit problem. In addition, a novel theoretical analysis on the upper bound of different objective functions helps us understand their bias to different community sizes, and experiments are conducted on both real life and simulated data to validate our findings.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 CommunityDetectioninGraphsthrou	Haibing Lu Lian Duan Willian Nick Street Yanchi Liu			Community Detection in Graphs through Correlation				10.1145/2623330.2623629		2014