Detection of influential nodes in social networks using SIR model

Influential node detection is one of the most important challenges in the social network analysis. The purpose of influential node detection is to find a set of individuals or organizations whose return of relationships to each other are greater than other network entities. Detection of influential nodes reveals the structure of groups in social networks and hidden communications between their components. The rapid growth of human networks has provided a suitable situation for the dissemination of new information, opinions, and ideas among network members with ever-increasing speed. With the formation of virtual social networks and the creation of wider and broader networks beyond geographical boundaries, a great and rich environment consists of various types of communication and network structures has been formed. By analyzing the structure of social networks, many researchers have tried to understand social networks and how they work. In social networks, one of the main issues is related to the dissemination of information and how it is performed. In this study, it is tried to find clusters using existing algorithms and then identify influential nodes within the clusters through centrality parameters. In the proposed approach, an algorithm is introduced to minimize the number of hops required for the dissemination of information across the entire social network.

Keywords:

Social networks , Influential node detection , clustering , SIR model