基于k -赢家通吃网络的社会系统延迟马太效应建模与分析

IF 7.9 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY

IEEE Transactions on Network Science and Engineering Pub Date : 2025-04-22 DOI:10.1109/TNSE.2025.3562075

Jiayi Wang;Suibing Li;Long Jin;Shuai Li

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引用次数: 0

摘要

本文建立了一个新的时滞马太效应模型，旨在描述社会系统中的马太效应。在构建具有信息传递延迟的社会网络的基础上，将社会网络中每个agent的发展速度和发展潜力定义为一些特定的参数，并用这些参数来描述每个agent的状态和进化趋势。进一步，从理论上推导了所提出的时滞马修效应模型在社会网络中允许的最大时滞值，并验证了其收敛性。通过一系列的仿真，验证了所提模型的正确性和可行性。本文首次在马太效应模型中考虑了信息传递的时间延迟，使该模型能够准确地描述这一社会现象。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Modeling and Analysis of Delayed Matthew Effect in Social Systems via $k$-Winners-Take-All Network

A new time-delayed Matthew effect model is developed in this paper, aiming to depict the Matthew effect in social systems. Based on the construction of a social network with information transmission delay, the development speed and development potential of each agent in the social network are defined as some specific parameters, and these parameters are used to describe the status and evolutionary trend of each agent. Furthermore, this paper theoretically derives the value of the maximum time delay allowed by the proposed time-delayed Matthew effect model in a social network and verifies its convergence. Through a series of simulations, the correctness and feasibility of the proposed model are demonstrated. This work takes the time delay in information transmission into account in the Matthew effect model for the first time so that the model is capable of describing this social phenomenon accurately.

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来源期刊

IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering

CiteScore

12.60

自引率

9.10%

发文量

393

期刊介绍： The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.