Recognition of opinion leaders coalitions in online social network using game theory

被引:15
|
作者
Jain, Lokesh [1 ]
Katarya, Rahul [1 ]
Sachdeva, Shelly [2 ]
机构
[1] Delhi Technol Univ, Dept Comp Sci & Engn, New Delhi, India
[2] Natl Inst Technol, Dept Comp Sci & Engn, New Delhi, India
关键词
Game theory; Opinion leader; Online Social Network; Shapley value; STRUCTURAL HOLES; SHAPLEY VALUE; TRUST; ALGORITHM; POWER;
D O I
10.1016/j.knosys.2020.106158
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Nowadays, human decision-making power and attitude revolutionize over time and change their viewpoint towards substantial convinced values because of the influence of social media. Social network plays a crucial role in human life with the progression of technology. In the online social network, the responsibility of opinion leaders is also vital in information diffusion and promotion purposes. In this manuscript, we proposed a different Game theory-based Opinion Leader Detection (GOLD) algorithm to identify the group of users having the maximum synergy declared as the coalition of opinion leaders. For implementing purpose, we used the game theory approach in which all the users behave like a player, and a trustor-trustee tree formed comprised coalitions based on trust and conditional probability. We proposed an inventive and distinctive solution to measures the individual payoff using the distance-based centrality parameter. We also computed the Shapley value for each user to identify the maximum marginal contribution and determined the maximum synergy of each coalition. The main advantage of the proposed approach is to strengthen the power of the coalition and produced the synergetic outcome. The proposed approach delivered around 90% accuracy and decreased 37% execution time. Therefore, it provides superior results regarding the accuracy, precision, time complexity, rate of convergence, and computational time with other SNA (Social Network Analysis) measures. (C) 2020 Elsevier B.V. All rights reserved.
引用
收藏
页数:20
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