Identification of influential spreaders in complex networks

被引:474
|
作者
Kitsak, Maksim [3 ,4 ,5 ]
Gallos, Lazaros K. [1 ,2 ]
Havlin, Shlomo [6 ,7 ]
Liljeros, Fredrik [8 ]
Muchnik, Lev [9 ]
Stanley, H. Eugene [3 ,4 ]
Makse, Hernan A. [1 ,2 ]
机构
[1] CUNY City Coll, Levich Inst, New York, NY 10031 USA
[2] CUNY City Coll, Dept Phys, New York, NY 10031 USA
[3] Boston Univ, Ctr Polymer Studies, Boston, MA 02215 USA
[4] Boston Univ, Dept Phys, Boston, MA 02215 USA
[5] Univ Calif San Diego, Cooperat Assoc Internet Data Anal CAIDA, La Jolla, CA 92093 USA
[6] Bar Ilan Univ, Minerva Ctr, Ramat Gan, Israel
[7] Bar Ilan Univ, Dept Phys, Ramat Gan, Israel
[8] Stockholm Univ, Dept Sociol, S-10691 Stockholm, Sweden
[9] NYU, Stern Sch Business, Informat Operat & Management Sci Dept, New York, NY 10012 USA
基金
以色列科学基金会; 美国国家科学基金会;
关键词
INTERNET; CENTRALITY;
D O I
10.1038/NPHYS1746
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Networks portray a multitude of interactions through which people meet, ideas are spread and infectious diseases propagate within a society(1-5). Identifying the most efficient 'spreaders' in a network is an important step towards optimizing the use of available resources and ensuring the more efficient spread of information. Here we show that, in contrast to common belief, there are plausible circumstances where the best spreaders do not correspond to the most highly connected or the most central people(6-10). Instead, we find that the most efficient spreaders are those located within the core of the network as identified by the k-shell decomposition analysis(11-13), and that when multiple spreaders are considered simultaneously the distance between them becomes the crucial parameter that determines the extent of the spreading. Furthermore, we show that infections persist in the high-k shells of the network in the case where recovered individuals do not develop immunity. Our analysis should provide a route for an optimal design of efficient dissemination strategies.
引用
收藏
页码:888 / 893
页数:6
相关论文
共 50 条
  • [1] An Entropy-Based Gravity Model for Influential Spreaders Identification in Complex Networks
    Liu, Yong
    Cheng, Zijun
    Li, Xiaoqin
    Wang, Zongshui
    COMPLEXITY, 2023, 2023
  • [2] Influential Spreaders Identification in Complex Networks With TOPSIS and K-Shell Decomposition
    Liu, Xiaoyang
    Ye, Shu
    Fiumara, Giacomo
    De Meo, Pasquale
    IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS, 2023, 10 (01): : 347 - 361
  • [3] Identify influential spreaders in complex networks, the role of neighborhood
    Liu, Ying
    Tang, Ming
    Zhou, Tao
    Do, Younghae
    PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2016, 452 : 289 - 298
  • [4] A New Method for Identifying Influential Spreaders in Complex Networks
    Qiu, Liqing
    Liu, Yuying
    Zhang, Jianyi
    COMPUTER JOURNAL, 2024, 67 (01) : 362 - 375
  • [5] Identification of influential spreaders based on classified neighbors in real-world complex networks
    Li, Chao
    Wang, Li
    Sun, Shiwen
    Xia, Chengyi
    APPLIED MATHEMATICS AND COMPUTATION, 2018, 320 : 512 - 523
  • [6] A new approach to identify influential spreaders in complex networks
    Hu Qing-Cheng
    Yin Yan-Shen
    Ma Peng-Fei
    Gao Yang
    Zhang Yong
    Xing Chun-Xiao
    ACTA PHYSICA SINICA, 2013, 62 (14)
  • [7] Identifying influential spreaders in complex networks by an improved gravity model
    Li, Zhe
    Huang, Xinyu
    SCIENTIFIC REPORTS, 2021, 11 (01)
  • [8] Identify influential spreaders in complex real-world networks
    Liu, Ying
    Tang, Ming
    Yue, Jing
    Gong, Jie
    IEEE 12TH INT CONF UBIQUITOUS INTELLIGENCE & COMP/IEEE 12TH INT CONF ADV & TRUSTED COMP/IEEE 15TH INT CONF SCALABLE COMP & COMMUN/IEEE INT CONF CLOUD & BIG DATA COMP/IEEE INT CONF INTERNET PEOPLE AND ASSOCIATED SYMPOSIA/WORKSHOPS, 2015, : 1144 - 1148
  • [9] Identifying influential spreaders in complex networks by propagation probability dynamics
    Chen, Duan-Bing
    Sun, Hong-Liang
    Tang, Qing
    Tian, Sheng-Zhao
    Xie, Mei
    CHAOS, 2019, 29 (03)
  • [10] SpreadRank: A Novel Approach for Identifying Influential Spreaders in Complex Networks
    Zhu, Xuejin
    Huang, Jie
    ENTROPY, 2023, 25 (04)