Identifying vital nodes from local and global perspectives in complex networks

被引:94
作者
Ullah, Aman [1 ]
Wang, Bin [1 ]
Sheng, JinFang [1 ]
Long, Jun [1 ,2 ]
Khan, Nasrullah [3 ,4 ]
Sun, ZeJun [5 ]
机构
[1] Cent South Univ, Sch Comp Sci & Engn, Changsha 410083, Peoples R China
[2] Cent South Univ, Big Data Inst, Changsha 410083, Peoples R China
[3] Nanjing Univ Aeronaut & Astronaut, Coll Comp Sci & Technol, Nanjing 210016, Peoples R China
[4] COMSATS Univ Islamabad, Dept Comp Sci, Vehari Campus, Vehari 61100, Pakistan
[5] Pingdingshan Univ, Sch Informat Engn, Pingdingshan 467000, Peoples R China
关键词
Vital nodes; Global and local information; Complex networks; INFLUENTIAL SPREADERS; CENTRALITY; IDENTIFICATION; RANKING;
D O I
10.1016/j.eswa.2021.115778
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Recognition of vital nodes in complex networks retains great importance in the improvement of network's robustness and vulnerability. Consistent research proposed various approaches like local-structure-based methods, e.g., degree centrality, pagerank, etc., and global-structure-based methods, e.g., betweenness, closeness centrality, etc., to evaluate the concerned nodes. Though their performance is amazingly well, these methods have undergone some intrinsic limitations. For instance, local-structure-based methods lose some sort of global information and global-structure-based methods are too complicated to measure the important nodes, particularly in networks where sizes become large. To tackle these challenges, we propose a Local-and-Global Centrality (LGC) measuring algorithm to identify the vital nodes through handling local as well as global topological aspects of a network simultaneously. In order to assess the performance of the proposed algorithm with respect to the state-of-the-art methodologies, we performed experiments through LCG, Betweenness (BNC), Closeness (CNC), Gravity (GIC), Page-Rank (PRC), Eigenvector (EVC), Global and Local Structure (GLS), Global Structure Model (GSM), and Profit-leader (PLC) methods on differently sized real-world networks. Our experiments disclose that LGC outperformed many of the compared techniques.
引用
收藏
页数:10
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