Detection of influential nodes using social networks analysis based on network metrics

Abstract

Now a days, social network analysis has a great theoretical and practical significance, and this area has been explored in many fields. Social network is composed of the actors and their interaction (relationship between them). Due to increased usage of social network analysis in the practical applications, identifying the nodes from the social network has become the major problem. In any social network, the behavior and role of any individual in term of key player decides the significance and importance of social media. Visualization and the analysis of the social network includes different metrics measures i.e. Coefficient Clustering, Density, Closeness Centrality, Degree Centrality, Page Rank, Eigenvector Centrality etc. These features can be used to visualize the social networks. These factors help us in the determination of the influential nodes in any network. Detection of the influential nodes (Key Players) in any large scale social media is a complex task as thousands of new users join the network every day. This paper puts an effort to visualize, calculate the different metrics of social network and on the basis of these values determine the most influential node. The experimental results and a detailed quantitative analysis shows that this is the more efficient and effective way to detect the influential nodes in a social network.