Enhanced Leach Algorithm for Opinion Leader Detection in Social Networks Using Centrality Metrics
Subject Areas : journal of Artificial Intelligence in Electrical Engineering
Yasser Elmi Sola
1
*
,
Afsaneh Razghandi
2
1 - دانشکده مهندسی کامپیوتر و فناوری اطلاعات، دانشگاه آزاد اسلامی، سبزوار ، ایران
2 -
Keywords: Social Network Analysis, Opinion Leaders, Wireless Sensor Networks, Leach Algorithm, Centrality,
Abstract :
Today, the development of human communication through social networks has increased the importance of social network analysis. In social networks, the role of nodes is not the same and important nodes play a key role in network performance. The nodes which have more influence in a social network are called "opinion leaders". Measuring the importance of each node is needed in order to finding the opinion leaders of social networks. This parameter is measured using the centrality criteria in the network’s graph. This paper presents an enhanced version of the Leach algorithm, originally designed for wireless sensor networks, to identify opinion leaders in social networks. By modifying the selection criteria for cluster heads and incorporating centrality measures (degree, closeness, betweenness, eigenvector), the proposed approach demonstrates a significant correlation with established centrality metrics, achieving correlation values above 0.7. This advancement underscores the importance of node influence in social dynamics and offers a robust method for analyzing key actors in complex networks. In this paper, the well-known Leach algorithm that used to find the cluster heads in wireless sensor network has been modified to determine opinion leaders in social networks. The evaluation of the proposed algorithm is performed using centrality (degree, closeness, betweenness and eigenvector) indicators. Simulation results show that the proposed algorithm has a significant relationship with the standard centrality indices with a correlation value greater than 0.7.
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