FastSNG: The Fastest Social Network Dataset Generator

Abstract

Large-scale social networks have become more and more popular with the rapid progress of social media. A number of social network analysis tasks have been developed to conduct on the real large-scale networks. However, the prohibitive cost of achieving the underlying large network, including time cost and data privacy, makes it hard to evaluate the performance of analysis algorithms on real-world social networks. In this paper, we present a tool called FastSNG, which generates heterogeneous social network datasets according to the user-defined configuration depicting the rich characteristics of the expected social network, such as community structures, attributes, and node degree distributions. Moreover, the generation algorithm of FastSNG adopts a degree distribution generation (D2G) model which is efficient to generate web-scale social network datasets. Finally, the tool provides user-friendly and succinct user interfaces for the interaction with general users.