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复杂网络中度数相关性对最小支配集规模的影响分析

Analysis of the Effect of Degree Correlation on the Size of Minimum Dominating Sets in Complex Networks.

作者信息

Takemoto Kazuhiro, Akutsu Tatsuya

机构信息

Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka, Japan.

Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto, Japan.

出版信息

PLoS One. 2016 Jun 21;11(6):e0157868. doi: 10.1371/journal.pone.0157868. eCollection 2016.

Abstract

Network controllability is an important topic in wide-ranging research fields. However, the relationship between controllability and network structure is poorly understood, although degree heterogeneity is known to determine the controllability. We focus on the size of a minimum dominating set (MDS), a measure of network controllability, and investigate the effect of degree-degree correlation, which is universally observed in real-world networks, on the size of an MDS. We show that disassortativity or negative degree-degree correlation reduces the size of an MDS using analytical treatments and numerical simulation, whereas positive correlations hardly affect the size of an MDS. This result suggests that disassortativity enhances network controllability. Furthermore, apart from the controllability issue, the developed techniques provide new ways of analyzing complex networks with degree-degree correlations.

摘要

网络可控性是广泛研究领域中的一个重要课题。然而,尽管已知度异质性决定可控性,但可控性与网络结构之间的关系却鲜为人知。我们关注最小支配集(MDS)的大小,它是网络可控性的一种度量,并研究度-度相关性(在现实世界网络中普遍存在)对MDS大小的影响。我们通过解析处理和数值模拟表明,异配性或负度-度相关性会减小MDS的大小,而正相关性几乎不影响MDS的大小。这一结果表明异配性增强了网络可控性。此外,除了可控性问题外,所开发的技术还为分析具有度-度相关性的复杂网络提供了新方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ad3/4915616/0e026d3fdf54/pone.0157868.g001.jpg

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