Random Graph Models and Their Application to Twitter Network Analysis

Abstract

In this paper, we conducted an experiment for comparison of the graphs generated by Erdős-Rényi, BarabásiAlbert, Bollobás-Riordan, Buckley–Osthus, Chung-Lu models and a web graph constructed using real data. Twitter data have been employed to construct social network, and C++ has been used for network analysis as well as network visualization. It was shown that distribution of degrees and clustering coefficient for this network follows the power law. A machine learning approach is used for empirical evaluation of the Erdős-Rényi, BarabásiAlbert, Bollobás-Riordan, Buckley–Osthus, Chung-Lu models in comparison to the Twitter graph.