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题名: Detectability Thresholds and Optimal Algorithms for Community Structure in Dynamic Networks
作者: Ghasemian, A ;  Zhang, P ;  Clauset, A ;  Moore, C ;  Peel, L
刊名: PHYSICAL REVIEW X
出版日期: 2016
卷号: 6, 期号:3, 页码:31005
学科分类: Physics
DOI: http://dx.doi.org/10.1103/PhysRevX.6.031005
通讯作者: Ghasemian, A (reprint author), Univ Colorado, Dept Comp Sci, Boulder, CO 80309 USA.
文章类型: Article
英文摘要: The detection of communities within a dynamic network is a common means for obtaining a coarse-grained view of a complex system and for investigating its underlying processes. While a number of methods have been proposed in the machine learning and physics literature, we lack a theoretical analysis of their strengths and weaknesses, or of the ultimate limits on when communities can be detected. Here, we study the fundamental limits of detecting community structure in dynamic networks. Specifically, we analyze the limits of detectability for a dynamic stochastic block model where nodes change their community memberships over time, but where edges are generated independently at each time step. Using the cavity method, we derive a precise detectability threshold as a function of the rate of change and the strength of the communities. Below this sharp threshold, we claim that no efficient algorithm can identify the communities better than chance. We then give two algorithms that are optimal in the sense that they succeed all the way down to this threshold. The first uses belief propagation, which gives asymptotically optimal accuracy, and the second is a fast spectral clustering algorithm, based on linearizing the belief propagation equations. These results extend our understanding of the limits of community detection in an important direction, and introduce new mathematical tools for similar extensions to networks with other types of auxiliary information.
类目[WOS]: Physics, Multidisciplinary
关键词[WOS]: STOCHASTIC BLOCKMODELS ;  RECONSTRUCTION ;  PREDICTION ;  MODEL
收录类别: SCI
项目资助者: BioFrontiers IT [NIH 1S10OD012300] ;  National Science Foundation [IIS-1452718] ;  U.S. Air Force Office of Scientific Research (AFOSR) [FA9550-12-1-0432] ;  Defense Advanced Research Projects Agency (DARPA) ;  Army Research Office (ARO) [W911NF-12-R-0012] ;  John Templeton Foundation
语种: 英语
Citation statistics: 
内容类型: 期刊论文
URI标识: http://ir.itp.ac.cn/handle/311006/21590
Appears in Collections:理论物理所2016年知识产出_期刊论文

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Recommended Citation:
Ghasemian, A,Zhang, P,Clauset, A,et al. Detectability Thresholds and Optimal Algorithms for Community Structure in Dynamic Networks[J]. PHYSICAL REVIEW X,2016,6(3):31005.
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