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一种从最小努力原则出发的与系统无关的优先连接推导。

A System-Independent Derivation of Preferential Attachment from the Principle of Least Effort.

作者信息

Machu François Xavier, Wang Ru Julie, Cheng Jean Louis, Cocks Jeremy, Wang Qiuping Alexandre

机构信息

Laboratoire SCIQ, ESIEA Group, 9 Rue Vésale, 75005 Paris, France.

IMMM, CNRS UMR 6283, Le Mans Université, Ave. O. Messiaen, 72085 Le Mans, France.

出版信息

Entropy (Basel). 2023 Feb 7;25(2):305. doi: 10.3390/e25020305.

DOI:10.3390/e25020305
PMID:36832671
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9955878/
Abstract

Preferential attachment (PA) is a widely observed behavior in many living systems and has been used in modeling many networks. The aim of this work is to show that the mechanism of PA is a consequence of the fundamental principle of least effort. We derive PA directly from this principle in maximizing an efficiency function. This approach not only allows a better understanding of the different PA mechanisms already reported but also naturally extends these mechanisms with a non-power law probability of attachment. The possibility of using the efficiency function as a general measure of attachment efficiency is also investigated.

摘要

偏好依附（PA）是许多生命系统中广泛观察到的一种行为，并已被用于许多网络的建模。这项工作的目的是表明，PA机制是最小努力基本原则的结果。我们在最大化一个效率函数时直接从这个原则推导出PA。这种方法不仅能更好地理解已报道的不同PA机制，还能自然地扩展这些机制，使其具有非幂律的依附概率。我们还研究了将效率函数用作依附效率通用度量的可能性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d410/9955878/fec8a2fd55dd/entropy-25-00305-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d410/9955878/47f85ad39ff8/entropy-25-00305-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d410/9955878/c322332cb0ff/entropy-25-00305-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d410/9955878/66efa52919ae/entropy-25-00305-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d410/9955878/fec8a2fd55dd/entropy-25-00305-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d410/9955878/47f85ad39ff8/entropy-25-00305-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d410/9955878/c322332cb0ff/entropy-25-00305-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d410/9955878/66efa52919ae/entropy-25-00305-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d410/9955878/fec8a2fd55dd/entropy-25-00305-g004.jpg

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本文引用的文献

1
PAFit: A Statistical Method for Measuring Preferential Attachment in Temporal Complex Networks.PAFit：一种用于测量时间复杂网络中优先连接的统计方法。
PLoS One. 2015 Sep 17;10(9):e0137796. doi: 10.1371/journal.pone.0137796. eCollection 2015.
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Growing optimal scale-free networks via likelihood.通过似然性生长最优无标度网络。
Phys Rev E Stat Nonlin Soft Matter Phys. 2015 Apr;91(4):042801. doi: 10.1103/PhysRevE.91.042801. Epub 2015 Apr 7.
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Scale-free networks as preasymptotic regimes of superlinear preferential attachment.
作为超线性偏好依附的前渐近状态的无标度网络。
Phys Rev E Stat Nonlin Soft Matter Phys. 2008 Aug;78(2 Pt 2):026114. doi: 10.1103/PhysRevE.78.026114. Epub 2008 Aug 21.
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