计算机科学
节点(物理)
中心性
复杂网络
谣言
鉴定(生物学)
路径(计算)
学位(音乐)
过程(计算)
最短路径问题
数据挖掘
理论计算机科学
图形
计算机网络
数学
植物
结构工程
生物
公共关系
操作系统
组合数学
物理
工程类
万维网
声学
政治学
作者
Jingcheng Zhu,Lunwen Wang
出处
期刊:Chinese Physics B
[IOP Publishing]
日期:2021-11-10
卷期号:31 (6): 068904-068904
被引量:7
标识
DOI:10.1088/1674-1056/ac380d
摘要
Accurate identification of influential nodes facilitates the control of rumor propagation and interrupts the spread of computer viruses. Many classical approaches have been proposed by researchers regarding different aspects. To explore the impact of location information in depth, this paper proposes an improved global structure model to characterize the influence of nodes. The method considers both the node’s self-information and the role of the location information of neighboring nodes. First, degree centrality of each node is calculated, and then degree value of each node is used to represent self-influence, and degree values of the neighbor layer nodes are divided by the power of the path length, which is path attenuation used to represent global influence. Finally, an extended improved global structure model that considers the nearest neighbor information after combining self-influence and global influence is proposed to identify influential nodes. In this paper, the propagation process of a real network is obtained by simulation with the SIR model, and the effectiveness of the proposed method is verified from two aspects of discrimination and accuracy. The experimental results show that the proposed method is more accurate in identifying influential nodes than other comparative methods with multiple networks.
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