Chaos, Solitons and Fractals: X最新文献_第9页

Brain noise estimation from MEG response to flickering visual stimulation 脑电信号对闪烁视觉刺激反应的脑噪声估计

Chaos, Solitons and Fractals: X Pub Date : 2019-03-01 DOI: 10.1016/j.csfx.2019.100005

Alexander N. Pisarchik , Parth Chholak , Alexander E. Hramov

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引用次数: 21

Self-induced transparency in a flux-qubit chain 磁通量子位链中的自感透明

Chaos, Solitons and Fractals: X Pub Date : 2019-03-01 DOI: 10.1016/j.csfx.2019.100003

Zoran Ivić , Nikos Lazarides , G.P. Tsironis

引用次数: 8

Fractional evolution in quantum mechanics 量子力学中的分数演化

Chaos, Solitons and Fractals: X Pub Date : 2019-03-01 DOI: 10.1016/j.csfx.2018.100001

A. Iomin

引用次数: 23

Chaos in historical prices and volatilities with five-dimensional euclidean spaces 五维欧几里得空间中历史价格和波动的混沌

Chaos, Solitons and Fractals: X Pub Date : 2019-03-01 DOI: 10.1016/j.csfx.2019.100002

P.R.L. Alves

引用次数: 9

Beyond the clustering coefficient: A topological analysis of node neighbourhoods in complex networks 超越聚类系数:复杂网络中节点邻域的拓扑分析

Chaos, Solitons and Fractals: X Pub Date : 2019-03-01 DOI: 10.1016/j.csfx.2019.100004

Alexander P. Kartun-Giles , Ginestra Bianconi

{"title":"Beyond the clustering coefficient: A topological analysis of node neighbourhoods in complex networks","authors":"Alexander P. Kartun-Giles , Ginestra Bianconi","doi":"10.1016/j.csfx.2019.100004","DOIUrl":"10.1016/j.csfx.2019.100004","url":null,"abstract":"<div><p>In Network Science, node neighbourhoods, also called ego-centered networks, have attracted significant attention. In particular the clustering coefficient has been extensively used to measure their local cohesiveness. In this paper, we show how, given two nodes with the same clustering coefficient, the topology of their neighbourhoods can be significantly different, which demonstrates the need to go beyond this simple characterization. We perform a large scale statistical analysis of the topology of node neighbourhoods of real networks by first constructing their clique complexes, and then computing their Betti numbers. We are able to show significant differences between the topology of node neighbourhoods of real networks and the stochastic topology of null models of random simplicial complexes revealing local organisation principles of the node neighbourhoods. Moreover we observe that a large scale statistical analysis of the topological properties of node neighbourhoods is able to clearly discriminate between power-law networks, and planar road networks.</p></div>","PeriodicalId":37147,"journal":{"name":"Chaos, Solitons and Fractals: X","volume":"1 ","pages":"Article 100004"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.csfx.2019.100004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84234424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 50