NDLI: A dynamical method to extract communities induced by low or middle-degree nodes

Content Provider	IEEE Xplore Digital Library
Author	Junhua Zhang Zhi-Ping Liu Xiang-Sun Zhang Luonan Chen
Copyright Year	2011
Description	Author affiliation: Key Laboratory of Random Complex Structures and Data Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China (Junhua Zhang) \|\| Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 200032, China (Zhi-Ping Liu; Luonan Chen) \|\| Key Laboratory of Management, Decision and Information Systems, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China (Xiang-Sun Zhang)
Abstract	Many networks are proved to have community structure. Dense communities have been intensively investigated in recent years, oppositely seldom attention has been paid to sparse ones, which refer to those communities induced by low or middle-degree nodes rather than high-degree components. Recently, it has gradually been recognized that sparse community is also an important structure in biological networks because most disease genes and drug targets are within it. In this paper, we propose a dynamical method to extract sparse communities in complex networks by constructing local synchronization properties of phase oscillators. Compared to dense communities, sparse ones provide more general building and functional blocks in the networks without emphasis on the dominance of internal degrees over outside ones as well as the constraints of high degree connectors.
Starting Page	340
Ending Page	344
File Size	925745
Page Count	5
File Format	PDF
ISBN	9781457716614
e-ISBN	9781457716669
e-ISBN	9781457716652
DOI	10.1109/ISB.2011.6033175
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-02
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Drugs Proteins Dynamical method Communities Local synchronization Humans Complex networks Complex network Synchronization Diseases Sparse community structure
Content Type	Text
Resource Type	Article

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