Measuring the centrality of nodes in networks based on the interstellar model

被引:0
|
作者
Chi, Kuo [1 ]
Wang, Ning [1 ]
Su, Ting [1 ]
Yang, Yongqin [1 ]
Qu, Hui [2 ]
机构
[1] Hainan Univ, Sch Informat & Commun Engn, Haikou 570228, Peoples R China
[2] Hainan Univ, Lab & Equipment Management Serv, Haikou 570228, Peoples R China
基金
中国国家自然科学基金;
关键词
Semi -global centrality metric; Social networks; Attraction force between nodes; Attraction slingshot effect; Interstellar model; Information dissemination; IDENTIFYING INFLUENTIAL NODES; SOCIAL NETWORKS; IDENTIFICATION; SPREADERS;
D O I
10.1016/j.ins.2024.120908
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Measuring the centrality of nodes in a network is a vital and significant task in analyzing the influence of nodes and information dissemination. Existing methods measure the centrality of nodes mainly from local, global and semi-global network perspectives. Currently, semi-global centrality metrics are valued due to the fact that they can achieve high accuracy close to that of global centrality metrics with a slight increase in time complexity over local centrality metrics. In this paper, a novel semi-global centrality metric based on the interstellar model is proposed. First, an interstellar model of information dissemination is constructed and a variable velocity is set for the information after it leaves the source node, while velocity at which the information arrives at the next node determines whether the information can be further forwarded. Then, the attraction slingshot effect is considered to express the facilitation provided by the nodes that forward the information. The centrality of a node can be measured by the number of all nodes, including itself, that can forward the information sent by that node. Experiments with some popular centrality metrics are conducted on some real-world networks, and the results show that the proposed centrality metric has better performance without significant increase in time complexity, and also provides a plausible explanation for information forwarding.
引用
收藏
页数:13
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