An Intuitionistic Fuzzy MCDM Approach Adapted to Minimum Spanning Tree Algorithm for Spreading Content on Social Media


ÇAKIR E. , ULUKAN H. Z.

11th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2021, Nevada, United States Of America, 27 - 30 January 2021, pp.174-179 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/ccwc51732.2021.9375942
  • City: Nevada
  • Country: United States Of America
  • Page Numbers: pp.174-179

Abstract

© 2021 IEEE.Social networks are platforms where users share their experiences and interact with each other. Unlike traditional web pages, users are not only passive consumers but also content producers and spreaders. Thus, it is necessary to benefit from the interactions between users in order to spread the information in the shortest and most effective way in social media networks. This research explores the idea of prioritization of social network connections by representing a social media network as undirected intuitionistic fuzzy weighted graph. The link weights are calculated by the intuitionistic fuzzy MCDM with the Hamacher aggregation operator. Then, the minimum spanning tree (or the highest weighted link for the most effective spreading) between all users is determined by the Kruskal algorithm. The proposed methodology contributes the literature by integrating Kruskal's algorithm with an intuitionistic fuzzy MCDM solution to solve MST problems in real life scenarios. To illustrate this hybrid approach, it is applied for spreading content on social media problem.