Analysis of social network metrics based on the model of random recursive tree

Abstract

Social network analysis can be defined as the method of examining social structures with the help of networks and graph theory.In this paper, we aim to develop a model which can replicate the features of social networks present in the real world, and can easily derivethe various characteristics associated with those networks. We define a model based on Random Recursive Tree (RRT) which is an extension to the Barabási-Albert model, capturing key features, such as the degree variance, the network diameter, the average path length, degree distribution etc. with respect to the size of the network and analyze the trends between these metrics. We then validate the proposed model by constructing various cascade trees with the help of a real-life social network, WeChat data-set.