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Roles of Clustering Coefficient for the Network Reconstruction

机译：聚类系数在网络重构中的作用

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摘要

It is important to establish relations between the network reconstruction and the topological dynamical structure of networks. In this article, we quantify the effect for two types of network topologies on the performance of network reconstruction. First, we generate two network modes with variable clustering coefficient based on Holme-Kim model and Newman-Watts small-world model, then we reconstruct the artificial networks by using a novel framework called L-1-norm minimization algorithm based on a theory called compressive sensing (CS), a framework for recovering sparse signals. The results of the simulation experiment show that the accuracy rate for the network reconstruction is a monotonically increasing function of the clustering coefficient in Holme-Kim model, whereas the opposite occurs in Newman-Watts small-world network. And this yet demonstrates that the larger the network size, the higher the accuracy rate. Morever, we compare the results of CS with orthogonal matching pursuit (OMP), a greedy algorithm. The results show that the accuracy rate of L-1-norm minimization method is 10% higher than that of OMP, and OMP yields 1.2 times the computation speed of L-1-norm minimization. Our work demonstrates that the topological structure of network has influence on the accurate reconstruction and it is helpful for offering proper method for the network reconstruction.
机译：建立网络重构与网络拓扑动态结构之间的关系非常重要。在本文中，我们量化了两种类型的网络拓扑对网络重建性能的影响。首先，我们基于Holme-Kim模型和Newman-Watts小世界模型生成两个具有可变聚类系数的网络模式，然后使用一种称为L-1-norm最小化算法的新颖框架，基于一种称为理论的网络，重构了人工网络。压缩感知（CS），一种用于恢复稀疏信号的框架。仿真实验结果表明，在Holme-Kim模型中，网络重构的准确率是聚类系数的单调递增函数，而在Newman-Watts小世界网络中则相反。而且这表明网络规模越大，准确率越高。此外，我们将CS的结果与一个贪婪算法正交匹配追踪（OMP）进行比较。结果表明，L-1-范数最小化方法的准确率比OMP高出10％，OMP的运算速度是L-1-范数最小化的1.2倍。我们的工作表明，网络的拓扑结构会影响准确的重建，有助于为网络重建提供合适的方法。

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  • 来源
    《Mathematical Problems in Engineering》 |2018年第14期|4949673.1-4949673.11|共11页
  • 作者

    Han Jingti; Mao Changmei;

  • 作者单位

    Shanghai Univ Finance & Econ, Sch Informat Management & Engn, Shanghai 200433, Peoples R China|Shanghai Univ Finance & Econ, Inst Fintech, Shanghai 200433, Peoples R China;

    Shanghai Univ Finance & Econ, Sch Informat Management & Engn, Shanghai 200433, Peoples R China|Shanghai Univ Finance & Econ, Inst Fintech, Shanghai 200433, Peoples R China;

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