Roles of clustering coefficient for the network reconstruction
type: Item
instance of: scholarly article
title: Roles of clustering coefficient for the network reconstruction
zbMATH Open document ID: 1427.92040
DOI: 10.1155/2018/4949673
author: Changmei Mao
author: Jing-ti Han
author: Ricardo L. Soto
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 92C42
Mathematics Subject Classification ID: 90B10
zbMATH DE Number: 7019052
describes a project that uses: IOTA
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2018/4949673
OpenAlex ID: W2898100354
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Wikidata QID: Q129047674
description: scientific article; zbMATH DE number 7019052