Open AccessIdentification of Complex Network Influencer using the Technology for Order Preference by Similarity to an Ideal Solution
Author(s) -
Khaoula Ait,
Tarik Agouti,
Mustapha Machkour,
Jilali Antari
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1743/1/012004
Subject(s) - topsis , centrality , influencer marketing , similarity (geometry) , computer science , betweenness centrality , preference , order (exchange) , ideal solution , rank (graph theory) , ranking (information retrieval) , identification (biology) , quality (philosophy) , ideal (ethics) , data mining , social network (sociolinguistics) , data science , marketing , information retrieval , social media , artificial intelligence , operations research , business , mathematics , world wide web , marketing management , statistics , political science , philosophy , image (mathematics) , biology , epistemology , relationship marketing , thermodynamics , physics , botany , finance , combinatorics , law
Marketing through social networks is a recent approach which consists in using these networks to convince potential consumers with the quality of products or services offered by a company. Marketing is developing very quickly, particularly on Facebook, Twitter, LinkedIn, Instagram, YouTube, etc. The major advantage of social networks is the possibility of influencing a panel of people according to their interests but without having the feeling of being guided. Identifying influencers is an interesting topic in social networks, and centrality measures are among the methods used to address this topic. Each measure has some shortcomings. In this paper, we gather centrality measures by using Technology for Order Preference by Similarity to an Ideal Solution (TOPSIS) method, which is a Multi-Criteria Decision Making (MCDM) to identify potential influences in a social network. A case study is presented to explain carefully TOPSIS and to illustrate the effectiveness of the proposed method, three real datasets are used for the experiments. The results show that TOPSIS can rank spreaders more accurately than centrality criteria.