A modified efficiency centrality to identify influential nodes in weighted networks

被引：34
|
作者：
Wang, Yunchuan [1 ]
Wang, Shasha [2 ]
Deng, Yong [3 ,4 ,5 ]
机构：
[1] Univ Elect Sci & Technol China, Sch Elect Sci & Engn, Chengdu, Sichuan, Peoples R China
[2] Sichuan Univ, Sch Comp Sci, Chengdu, Sichuan, Peoples R China
[3] Univ Elect Sci & Technol China, Inst Fundamental & Frontier Sci, Chengdu, Sichuan, Peoples R China
[4] Jinan Univ, Big Data Decis Inst, Guangzhou, Guangdong, Peoples R China
[5] Southwest Univ, Sch Comp & Informat Sci, Chongqing, Peoples R China
来源：
PRAMANA-JOURNAL OF PHYSICS | 2019年 / 92卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Complex network; influential nodes; weighted network; efficiency centrality; COMPLEX NETWORKS; LARGE-SCALE; DYNAMICS; SYNCHRONIZATION; IDENTIFICATION;
D O I：
10.1007/s12043-019-1727-1
中图分类号：
O4 [物理学];
学科分类号：
0702 ;
摘要：
It is still a crucial issue to identify influential nodes effectively in the study of complex networks. As for the existing efficiency centrality (EffC), it cannot be applied to a weighted network. In this paper, a modified efficiency centrality (EffC) is proposed by extending EffC into weighted networks. The proposed measure trades off the node degree and global structure in a weighted network. The influence of both the sum of the average degree of nodes in the whole network and the average distance of the network is taken into account. Numerical examples are used to illustrate the efficiency of the proposed method.
引用
收藏
页数：11
相关论文
共 50 条
  • [41] Identifying influential nodes on directed networks
    Lee, Yan-Li
    Wen, Yi-Fei
    Xie, Wen -Bo
    Pan, Liming
    Du, Yajun
    Zhou, Tao
    INFORMATION SCIENCES, 2024, 677
  • [42] LCH: A local clustering H-index centrality measure for identifying and ranking influential nodes in complex networks*
    Xu, Gui-Qiong
    Meng, Lei
    Tu, Deng-Qin
    Yang, Ping-Le
    CHINESE PHYSICS B, 2021, 30 (08)
  • [43] GPN: A novel gravity model based on position and neighborhood to identify influential nodes in complex networks
    Tu, Dengqin
    Xu, Guiqiong
    Meng, Lei
    INTERNATIONAL JOURNAL OF MODERN PHYSICS B, 2021, 35 (17):
  • [44] Towards identifying influential nodes in complex networks using semi-local centrality metrics
    Zhang, Kun
    Zhou, Yu
    Long, Haixia
    Wang, Chaoyang
    Hong, Haizhuang
    Armaghan, Seyed Mostafa
    JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES, 2023, 35 (10)
  • [45] Identifying influential nodes in weighted complex networks using an improved WVoteRank approach
    Kumar, Sanjay
    Panda, Ankit
    APPLIED INTELLIGENCE, 2022, 52 (02) : 1838 - 1852
  • [46] Embedding model of multilayer networks structure and its application to identify influential nodes
    Lei, Mingli
    Cheong, Kang Hao
    INFORMATION SCIENCES, 2024, 661
  • [47] Evidential identification of influential nodes in network of networks
    Li, Meizhu
    Zhang, Qi
    Deng, Yong
    CHAOS SOLITONS & FRACTALS, 2018, 117 : 283 - 296
  • [48] Detecting Influential Nodes with Centrality Measures via Random Forest in Social Networks
    Aidara, Ndeye Khady
    Diop, Issa Moussa
    Diallo, Cherif
    Cherifi, Hocine
    2024 IEEE WORKSHOP ON COMPLEXITY IN ENGINEERING, COMPENG 2024, 2024,
  • [49] Finding influential nodes via graph embedding and hybrid centrality in complex networks
    Ullah, Aman
    Meng, Yahui
    CHAOS SOLITONS & FRACTALS, 2025, 194
  • [50] A new method to identify influential nodes based on relative entropy
    Fei, Liguo
    Deng, Yong
    CHAOS SOLITONS & FRACTALS, 2017, 104 : 257 - 267
12345