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A new semi-local centrality for identifying influential nodes based on local average shortest path with extended neighborhood

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成果类型:
期刊论文
作者:
Xiao, Yi;Chen, Yuan;Zhang, Hongyan;Zhu, Xinghui;Yang, Yimin*;...
通讯作者:
Yang, Yimin;Zhu, XP
作者机构:
[Zhang, Hongyan; Zhu, Xinghui; Xiao, Yi] Hunan Agr Univ, Coll Informat & Intelligence, Changsha 410128, Hunan, Peoples R China.
[Zhang, Hongyan; Zhu, Xinghui; Chen, Yuan; Xiao, Yi] Hunan Engn & Technol Res Ctr Agr Big Data Anal & D, Changsha 410128, Hunan, Peoples R China.
[Yang, Yimin; Zhu, Xiaoping; Zhu, XP] Hunan Agr Univ, Business Sch, Changsha 410128, Hunan, Peoples R China.
通讯机构:
[Yang, YM; Zhu, XP ] H
Hunan Agr Univ, Business Sch, Changsha 410128, Hunan, Peoples R China.
语种:
英文
关键词:
Influential node;Centrality;Semi-local metric;Kendall's coefficient;Average shortest path
期刊:
Artificial Intelligence Review
ISSN:
0269-2821
年:
2024
卷:
57
期:
5
页码:
1-21
基金类别:
Hunan Province Key R&D Program Project: Research and Application Demonstration of Intelligent Service Technology and System for Agricultural Experts [2020NK2033]
机构署名:
本校为第一且通讯机构
院系归属:
商学院
摘要:
AbstractQuantifying the importance of nodes in complex networks is known as the problem of identifying influential nodes and is considered a critical aspect in interacting with these networks. This problem has many applications such as controlling rumors, sickness spreading, and viral marketing, where its importance has been understood by the research society in the last decade. This paper proposes a new semi-local centrality to identify influential nodes in complex networks based on the theory of Local Average Shortest Path with extended Neigh...

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