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
  • [41] Finding the Trustworthiness Nodes from Signed Social Networks
    Li, Hui
    Zhang, Shu
    Wang, Xia
    JOURNAL OF INTELLIGENT SYSTEMS, 2013, 22 (04) : 471 - 485
  • [42] Efficient routing in social DTN based on nodes' movement prediction
    Zhang, Zhen-Jing
    Jin, Zhi-Gang
    Shu, Yan-Tai
    Jisuanji Xuebao/Chinese Journal of Computers, 2013, 36 (03): : 626 - 635
  • [43] Detecting Common Interest Kernels in Large Social Networks
    Hu, Weishu
    Hou, Leong U.
    Gong, Zhiguo
    2012 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY (WI-IAT 2012), VOL 1, 2012, : 724 - 731
  • [44] A Belief Approach for Detecting Spammed Links in Social Networks
    Ben Dhaou, Salma
    Kharoune, Mouloud
    Martin, Arnaud
    Ben Yaghlane, Boutheina
    PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE (ICAART), VOL 2, 2019, : 602 - 609
  • [45] Detecting misinformation in social networks using provenance data
    Baeth, Mohamed Jehad
    Aktas, Mehmet S.
    CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2019, 31 (03)
  • [46] Detecting Community Structures in Social Networks by Graph Sparsification
    Basuchowdhuri, Partha
    Sikdar, Satyaki
    Shreshtha, Sonu
    Majumder, Subhashis
    PROCEEDINGS OF THE THIRD ACM IKDD CONFERENCE ON DATA SCIENCES (CODS), 2016,
  • [47] Evaluating Influential Nodes in Social Networks by Local Centrality with a Coefficient
    Zhao, Xiaohui
    Liu, Fang'ai
    Wang, Jinlong
    Li, Tianlai
    ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2017, 6 (02)
  • [48] NodeRank: Finding influential nodes in social networks based on interests
    Mohammed Bahutair
    Zaher Al Aghbari
    Ibrahim Kamel
    The Journal of Supercomputing, 2022, 78 : 2098 - 2124
  • [49] Learning Latent Representations of Nodes for Classifying in Heterogeneous Social Networks
    Jacob, Yann
    Denoyer, Ludovic
    Gallinari, Patrick
    WSDM'14: PROCEEDINGS OF THE 7TH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, 2014, : 373 - 382
  • [50] Extracting Influential Nodes in Social Networks on Local Weight Aspect
    Cheng, Jun Jun
    Zhang, Yan Chao
    Zhou, Xin
    Cheng, Hui
    INTERNATIONAL JOURNAL OF INTERDISCIPLINARY TELECOMMUNICATIONS AND NETWORKING, 2016, 8 (02) : 21 - 35