M.A. Thesis:

Detecting of opinion leaders in social networks

Considering everyday high growth of social communications through electronic devices, the analysis of these social networks have become so important. The condition of nodes are not the same in a social network and there is a lot of difference among their values. In general, important nodes play a key role in network function. In studies, different names are used to call the effective nodes. In most articles, the nodes are called "opinion leader" and in the rest, they are called "influential people", "market professionals" and "key actors". The process of calculating the importance of nodes is a fundamental problem to find the most important node. In social networks studies, the importance of nodes will be calculated by the centrality standards. This study goes through analysis of social networks using the centrality concept (degree centrality, closeness centrality, betweenness centrality, eigenvector centrality) and using LEACH algorithm for clustering and choosing the head of cluster in wireless sensor networks. The goal of this study is the using the well-known LEACH algorithm in social networks area. Desirable revenue of this algorithm in choosing the cluster head in a wireless sensor network caused to modify and use it to find the opinion leaders in social networks. The simulation results show that the proposed algorithm with standard centrality indicators has meaningful logical relation and correlation of their outputs are more than 0.7.

Keywords: Social network, Centrality, LEACH algorithm, Cluster head