Generating Simple Directed Social Network Graphs for Information Spreading
Christoph Schweimer, Christine Gfrerer, Florian Lugstein, Dave Pape, Jan A. Velimsky, Robert Elsässer⋆, Bernhard C. Geiger
Abstract
Online social networks are a dominant medium in everyday life to stay in contact with friends and to share information. In Twitter, users can connect with other users by following them, who in turn can follow back. In recent years, researchers studied several properties of social networks and designed random graph models to describe them. Many of these approaches either focus on the generation of undirected graphs or on the creation of directed graphs without modeling the dependencies between reciprocal (i.e., two directed edges of opposite direction between two nodes) and directed edges. We propose an approach to generate directed social network graphs that creates reciprocal and directed edges and considers the correlation between the respective degree sequences.