NDLI: An Efficient Spectral Algorithm for Network Community Discovery and Its Applications to Biological and Social Networks

Content Provider	IEEE Xplore Digital Library
Author	Jianhua Ruan Weixiong Zhang
Copyright Year	2007
Description	Author affiliation: Washington Univ. in St Loius, St. Louis (Jianhua Ruan; Weixiong Zhang)
Abstract	Automatic discovery of community structures in complex networks is a fundamental task in many disciplines, including social science, engineering, and biology. A quantitative measure called modularity (Q) has been proposed to effectively assess the quality of community structures. Several community discovery algorithms have since been developed based on the optimization of Q. However, this optimization problem is NP-hard, and the existing algorithms have a low accuracy or are computationally expensive. In this paper, we present an efficient spectral algorithm for modularity optimization. When tested on a large number of synthetic or real-world networks, and compared to the existing algorithms, our method is efficient and and has a high accuracy. In addition, we have successfully applied our algorithm to detect interesting and meaningful community structures from real-world networks in different domains, including biology, medicine and social science. Due to space limitation, results of these applications are presented in a complete version of the paper available on our Website (http://cse .wustl.edu/ $^{~}jruan/).$
Starting Page	643
Ending Page	648
File Size	491892
Page Count	6
File Format	PDF
ISBN	9780769530185
ISSN	15504786
DOI	10.1109/ICDM.2007.72
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Social network services Partitioning algorithms Clustering algorithms Biology Complex networks Laplace equations Samarium Data engineering Q measurement Space technology
Content Type	Text
Resource Type	Article

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