1时,模型的知识是永久的。此外,为了补充理论分析,本文对四种典型的网络模型:随机规则网络、小世界网络、随机增长网络和无标度网络进行了数值模拟。仿真结果表明,评审机制对四个网络中的知识传递有明显的正向影响,即评审率越高,福音节点的最终密度越高。此外,仿真结果表明,无标度网络比其他三种网络传递知识的速度更快。" />

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Review mechanism promotes knowledge transmission in complex networks

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作者:Wang, HY (Wang, Haiying)[ 1 ] ; Wang, J (Wang, Jun)[ 1 ] ; Small, M (Small, Michael)[ 2,3 ] ; Moore, JM (Moore, Jack Murdoch)[ 2 ]

APPLIED MATHEMATICS AND COMPUTATION

卷: 340页: 113-125

DOI: 10.1016/j.amc.2018.07.051

出版年: JAN 1 2019

文献类型:Review

摘要

Knowledge transmission systems differ from epidemic spreading systems in that people who have forgotten knowledge can reacquire it through reviewing. In order to analyze the review mechanism, we propose a Naive-Evangelical-Agnostic (VEA) knowledge transmission model in complex networks. Specifically, we derive a knowledge transmission system in homogeneous and heterogeneous networks, respectively. Mean field theory is used to theoretically delineate the knowledge transmission systems. In homogeneous networks, the steady state solution of the system is obtained. In heterogeneous networks, we get the basic reproduction number R-0, in which the reviewing rate is an important parameter. Moreover, we analyze the system and prove that if R-0 < 1, the knowledge loss equilibrium of the model is globally asymptotically stable; if R-0 > 1, the knowledge is permanent. In addition, to complement the theoretical analysis, numerical simulations are performed in four representative network models: random regular, small world, random growth and scale free networks. The simulation results indicate that the review mechanism has a clear positive influence for the knowledge transmission in the four networks, i.e., a higher reviewing rate leads to a higher final density of evangelical nodes. In addition, the simulation results illustrate that scale free networks transfer knowledge faster than the other three networks. (C) 2018 Elsevier Inc. All rights reserved.

关键词

作者关键词:Knowledge transmission; Review mechanism; Complex networks; Equilibrium

KeyWords Plus:SCALE-FREE NETWORKS; SOCIAL NETWORKS; MODEL; DIFFUSION; EMERGENCE; DYNAMICS; PATTERNS; MEMORY

通讯作者地址:

Beihang University Beihang Univ, Sch Econ & Management, Beijing 100191, Peoples R China.

通讯作者地址: Wang, J (通讯作者)