A Proximity-Based Generative Model for Online Social Network Topologies

Online social networks (OSN) are an increasingly powerful force for information diffusion and opinion sharing in society. Thus, understanding and modeling their structure and behavior is critical. Researchers need vast databases of self-contained, appropriately-sized OSN topologies in order to test and train new algorithms and models to solve problems related to these platforms. In this paper, we present a flexible, robust, and novel model for generating synthetic networks which closely resemble real OSN network systems (e.g., Facebook and Twitter) that include community structures. We also present an automated parameter tuner which can match the model’s output to a given OSN topology. The model can then be used as a data factory to generate testbeds of synthetic topologies which closely resemble the given sample. We compare our model, tuned to match two large real-world OSN network samples, with the Barabási-Albert model and the Lancichinetti-Fortunato-Radicchi benchmark used as baselines. We find that output of our proposed generative model more closely matches the target topologies, than either model, on a variety of important metrics — including clustering coefficient, modularity, assortativity, and average path length. Our model also organically generates robust, realistic communities, with non-trivial inter- and intra-community structure.