计算机应用 ›› 2019, Vol. 39 ›› Issue (10): 2966-2972.DOI: 10.11772/j.issn.1001-9081.2019040664

• 网络空间安全 • 上一篇    下一篇

网络负能量传播动力模型及仿真

刘超, 黄诗雯, 杨宏雨, 曹琼, 刘小洋   

  1. 重庆理工大学 计算机科学与工程学院, 重庆 400054
  • 收稿日期:2019-04-19 修回日期:2019-06-19 出版日期:2019-10-10 发布日期:2019-08-21
  • 通讯作者: 黄诗雯
  • 作者简介:刘超(1983-),男,四川广安人,副教授,博士,主要研究方向:信息传播;黄诗雯(1995-),女,重庆人,硕士研究生,主要研究方向:信息传播;杨宏雨(1977-),女,河南安阳人,副教授,硕士,主要研究方向:信息传播;曹琼(1979-),女,四川邻水人,讲师,硕士,主要研究方向:信息传播;刘小洋(1980-),男,安徽安庆人,副教授,博士,主要研究方向:信息传播。
  • 基金资助:
    重庆市教育委员会人文社会科学研究项目(17SKG151,18SKSZ028,18SKGH100)。

Network negative energy propagation dynamics model and simulation

LIU Chao, HUANG Shiwen, YANG Hongyu, CAO Qiong, LIU Xiaoyang   

  1. College of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, China
  • Received:2019-04-19 Revised:2019-06-19 Online:2019-10-10 Published:2019-08-21
  • Supported by:
    This work is partially supported by the Fund of Humanities and Social Sciences of the Chongqing Municipal Education Committee (17SKG151, 18SKSZ028, 18SKGH100).

摘要: 针对现有研究没有考虑将网络负能量传播的影响因素细化并构建传播动力学模型进行分析的问题,提出网络负能量传播的弱免疫-强免疫-弱感染-强感染-恶意感染(WSRIE)模型。首先,考虑网络用户对网络负能量免疫能力、传播能力的差异性,将易感染状态划分为弱免疫状态和强免疫状态,将感染状态划分为弱感染状态、强感染状态以及规模保持不变的恶意传播状态;其次,根据网络负能量的感染机制,提出状态转移规律;最后,构建了面向复杂网络的网络负能量传播动力学模型。在NW小世界网络和BA无标度网络上进行仿真对比实验。仿真结果表明,在同样参数的情况下,NW网络的弱免疫节点密度比BA网络低9个百分点,说明具备小世界特征的网络更易受负能量的影响;BA网络中恶意节点度为200时的感染节点密度比其节点度为0时高5个百分点,说明网红意见领袖的节点度越大,受网络负能量影响的网络用户更多。

关键词: 网络负能量, 传播模型, 小世界, 无标度, 节点度

Abstract: In view of the problem that the existing researches do not consider the refinement of the factors affecting the network negative energy propagation and construct a propagation dynamics model for analysis, a Weak-Strong-Received-Infected-Evil (WSRIE) model of network negative energy propagation was proposed. Firstly, considering the difference of negative energy immunity and propagation ability of network users, the vulnerable states were divided into weak immunity and strong immunity, and the infectious states were divided into weak infection, strong infection and malicious propagation with unchanged scale. Secondly, according to the negative energy infection mechanism of the network, the state transition law was proposed. Finally, a dynamics model of network negative energy propagation for complex networks was constructed. The simulation comparison experiments on NW small world network and BA scale-free network were carried out. The simulation results show that under the same parameters, the weak immune node density of the NW network is 9 percentage points lower than that of the BA network, indicating that the network with small world characteristics is more susceptible to negative energy. In the BA network, the density of infected nodes with the malicious node degree of 200 is 5 percentage points higher than that with the node degree of 0, indicating that the greater the node degree of the network red opinion leader, the more network users affected by the network negative energy.

Key words: network negative energy, propagation model, small world, scale-free, node degree

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