无权网络零模型的构造及应用
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  • 英文篇名:Construction and Applications of Null Models for Unweighted Networks
  • 作者:许小可 ; 崔文阔 ; 崔丽艳 ; 肖婧 ; 尚可可
  • 英文作者:XU Xiao-ke;CUI Wen-kuo;CUI Li-yan;XIAO Jing;SHANG Ke-ke;College of Information and Communication Engineering, Dalian Minzu University;Guizhou Provincial Key Laboratory of Public Big Data (Guizhou University);Computational Communication Collaboratory School of Journalism and Communication, Nanjing University;
  • 关键词:配置模型 ; 零模 ; 随机断边重连 ; 无权网络
  • 英文关键词:configuration model;;null model;;random disconnect-rewiring;;unweighted network
  • 中文刊名:DKDX
  • 英文刊名:Journal of University of Electronic Science and Technology of China
  • 机构:大连民族大学信息与通信工程学院;贵州省公共大数据重点实验室(贵州大学);南京大学新闻传播学院计算机传播实验中心;
  • 出版日期:2019-01-30
  • 出版单位:电子科技大学学报
  • 年:2019
  • 期:v.48
  • 基金:国家自然科学基金(61773091,61603073,61374170);; 辽宁省高等学校创新人才支持计划(DC201502060201);; 辽宁省重点研发计划指导计划项目(2018104016)
  • 语种:中文;
  • 页:DKDX201901020
  • 页数:20
  • CN:01
  • ISSN:51-1207/T
  • 分类号:124-143
摘要
静态无权网络是目前最常见的复杂网络形式,这种网络零模型也被研究得最广泛和最深入。该文将无权网络分成无权无向网络和无权有向网络两种形式,分别研究了这两类网络的零模型构造及应用,其中重点是无权无向网络。首先根据不同阶数随机图理论阐述了无权无向网络由低到高各阶零模型的定义,然后描述了使用ER随机图、配置模型和基于断边重连等方式构造各阶零模型的过程及相关应用。针对断边重连这种最重要的零模型构造方式,论述了无倾向性断边重连、有倾向性同配或异配断边重连,以及检测网络是否具有富人俱乐部性质的局部断边重连等构造方式,并且首次将高阶零模型扩展到社团检测等网络中尺度特性的分析中。最后,阐述了无权有向网络1阶零模型的构造以及如何基于该零模型检测网络中存在的出入度匹配特性。该文发现网络零模型能为实证无权网络提供一个准确的基准,结合网络的统计量指标定性和定量地描述出实际复杂网络的非平凡特性以及这种非平凡特性的程度及来源。
        The static unweighted network, whose null models have been studied widely and thoroughly, is the most common type of complex networks at present. In this study, we classify an unweighted network into two types of networks: undirected network and directed network. We study the construction and applications of null models for the two kinds of networks, especially the null models of unweighted undirected network are emphasized. Firstly, we illustrate the definitions of null models from low to high orders for unweighted undirected network according to the theory of random graph series. And then we describe the constructing process and related applications for 1 k-3 k null models by using ER random graph, configuration model, edge swapping, and so on. For the edge swapping algorithm, which is the most important mode for constructing null models, we introduce non-tendentious random edge swapping, tendentious assortative or dis-assortative edge swapping, and local edge swapping for detecting whether the rich-club properties exist in a network. Moreover, the high order null models are firstly extended to analyze meso-scale network features such as community detection. Finally, we analyze 1 k null models of directed network and tried to detect four types of in-out degree assortativities. In this study, we find that null models can not only provide an accurate baseline for real-life networks, but also qualitatively and quantitatively describe non-trivial properties of empirical complex networks.
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