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Edge-based stochastic network model reveals structural complexity of edges

机译：基于边缘的随机网络模型揭示了边缘的结构复杂性

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摘要

Network Science defines complex systems as objects interacting in a network with nodes and edges. Stochastic network models that treat networks as a collection of nodes with fixed degree distributions and randomly-connected edges have provided significant theoretical support for network analyses. However, the structural characteristics of edges in complex networks remain largely unknown due to the lack of edge-based network models. Here, we propose a general edge-based stochastic network model with constrained edge-degree distributions and arbitrary node-degree distributions. The random edge configuration method is used to build the model with the explicit edge-connected probability, which can also be explained by Laplacian dynamics. The model reveals both basic and complex structural characteristics of edges in networks, including statistical structural characteristics, link community structure, and higher-order organization. The experimental results show the advantageous performance on both link community and motifs detection based on the edge-based stochastic network model, which demonstrate that the model is useful for conducting quantitative comparisons the complex structural characteristics of edges. The edge-based stochastic network model is fundamental model to help understand the complex structure of edges that is hard to quantify in the complex networks. (C) 2019 Elsevier B.V. All rights reserved.
机译：网络科学将复杂的系统定义为对象在网络中与节点和边缘进行交互的对象。将网络视为具有固定程度分布和随机连接的边的节点集合的随机网络模型，为网络分析提供了重要的理论支持。但是，由于缺乏基于边缘的网络模型，因此复杂网络中边缘的结构特征仍然未知。在此，我们提出了一种具有约束边缘度分布和任意节点度分布的基于边缘的通用随机网络模型。随机边缘配置方法用于建立具有显式边缘连接概率的模型，这也可以用拉普拉斯动力学来解释。该模型揭示了网络边缘的基本和复杂结构特征，包括统计结构特征，链接社区结构和高阶组织。实验结果表明，基于边缘的随机网络模型在链接社区和图案检测方面均具有优势，证明该模型可用于对边缘的复杂结构特征进行定量比较。基于边缘的随机网络模型是帮助理解复杂网络中难以量化的边缘复杂结构的基本模型。 （C）2019 Elsevier B.V.保留所有权利。

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  • 来源
    《Future generation computer systems》 |2019年第11期|1073-1087|共15页
  • 作者

    Zhai Xuemeng; Zhou Wanlei; Fei Gaolei; Lu Cai; Wen Sheng; Hu Guangmin;

  • 作者单位

    Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Sichuan Peoples R China;

    Univ Technol Sydney Sch Software Ultimo NSW 2007 Australia;

    Swinburne Univ Technol Sch Software & Elect Engn Hawthorn Vic 3122 Australia;

    Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Sichuan Peoples R China|Univ Elect Sci & Technol China Ctr Informat Geosci Chengdu 611731 Sichuan Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Complex network; Stochastic network model; Link community detection; Motifs;

    机译：复杂的网络;随机网络模型;链接社区检测;主题;

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