Launcher nodes for detecting efficient influencers in social networks

被引:0
|
作者
Martins P. [1 ,2 ]
Martins F.A. [3 ]
机构
[1] Coimbra Business School - ISCAC, Polytechnic Institute of Coimbra
[2] Centro de Matemática, Aplicações Fundamentais e Investigação Operacional (CMAFcIO), Universidade de Lisboa, Lisboa
来源
Online Social Networks and Media | 2021年 / 25卷
关键词
Influence propagation; Influencers; Message viral power; Social networks;
D O I
10.1016/j.osnem.2021.100157
中图分类号
学科分类号
摘要
Influence propagation in social networks is a subject of growing interest. A relevant issue in those networks involves the identification of key influencers. These players have an important role on viral marketing strategies and message propagation, including political propaganda and fake news. In effect, an important way to fight malicious usage on social networks is to understand their properties, their structure and the way messages propagate. This paper proposes a new index for analyzing message propagation in social networks, based on the network topological nature and the influential power of the message. The new index characterizes the strength of each node as a launcher of the message, dividing the nodes into launchers and non-launchers. This division is most evident when the viral power of the message is high. Together with other known metrics, launcher individuals can assist to select efficient influencers in a social network. For instance, instead of choosing a strong member according to its degree in the network (number of followers), we may previously select those belonging to the launchers group and then look for the lowest degree members contained therein. These members are probably cheaper (on financial incentives) but still guarantying almost the same influence effectiveness as the largest degree members. We discuss this index using a number of real-world social networks available in known datasets repositories. © 2021
引用
收藏
相关论文
共 50 条
  • [1] Detecting Communities and Corresponding Central Nodes in Large Social Networks
    Jiang, Shengyi
    Wu, Meiling
    HIGH PERFORMANCE COMPUTING, 2013, 207 : 55 - 65
  • [2] The Relationship of Chilean Minors with Brands and Influencers on Social Networks
    Feijoo, Beatriz
    Sadaba, Charo
    SUSTAINABILITY, 2021, 13 (05) : 1 - 14
  • [3] The Impact of Social Diversity and Dynamic Influence Propagation for Identifying Influencers in Social Networks
    Huang, Pei-Ying
    Liu, Hsin-Yu
    Chen, Chin-Hui
    Cheng, Pu-Jen
    2013 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCES ON WEB INTELLIGENCE (WI) AND INTELLIGENT AGENT TECHNOLOGIES (IAT), VOL 1, 2013, : 410 - 416
  • [4] Efficient Selection of Influential Nodes for Viral Marketing in Social Networks
    Menta, Venkata Pushpak Teja
    Singh, Parikshit Kishor
    2017 IEEE INTERNATIONAL CONFERENCE ON CURRENT TRENDS IN ADVANCED COMPUTING (ICCTAC), 2017,
  • [5] MCD: A modified community diversity approach for detecting influential nodes in social networks
    Gupta, Aaryan
    Khatri, Inder
    Choudhry, Arjun
    Kumar, Sanjay
    JOURNAL OF INTELLIGENT INFORMATION SYSTEMS, 2023, 61 (02) : 473 - 495
  • [6] MCD: A modified community diversity approach for detecting influential nodes in social networks
    Aaryan Gupta
    Inder Khatri
    Arjun Choudhry
    Sanjay Kumar
    Journal of Intelligent Information Systems, 2023, 61 : 473 - 495
  • [7] DISSEMINATORS, NOT INFLUENCERS: COMMUNICATION OF DIETITIANS ON SOCIAL NETWORKS
    Marauri-Castillo, Inigo
    Rodriguez-Gonzalez, Maria del Mar
    Marin-Murillo, Flora
    VIVAT ACADEMIA, 2024, (157):
  • [8] Identifying influencers from sampled social networks
    Tsugawa, Sho
    Kimura, Kazuma
    PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2018, 507 : 294 - 303
  • [9] An Analytical Way to Find Influencers on Social Networks and Validate their Effects in Disseminating Social Games
    Kim, 'Erica' Suyeon
    Han, 'Steve' Sangki
    2009 INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING, 2009, : 41 - 46
  • [10] Efficient parallel algorithm for detecting influential nodes in large biological networks on the Graphics Processing Unit
    Xiao, Lei
    Wang, Shuangyan
    Mei, Gang
    FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2020, 106 : 1 - 13