New methods to generate massive synthetic networks

One of the biggest needs in network science research is access to large realistic datasets. As data analytics methods permeate a range of diverse disciplines—e.g., computational epidemiology, sustainability, social media analytics, biology, and transportation— network datasets that can exhibit characteristics encountered in each of these disciplines becomes paramount. Œe key technical issue is to be able to generate synthetic topologies with pre-speci€ed, arbitrary, degree distributions. Existing methods are limited in their ability to faithfully reproduce macro-level characteristics of networks while at the same time respecting particular degree distributions. We present a suite of three algorithms that exploit the principle of residual degree aŠenuation to generate synthetic topologies that adhere to macro-level real-world characteristics. By evaluating these algorithms w.r.t. several real-world datasets we demonstrate their ability to faithfully reproduce network characteristics such as node degree, clustering coecient, hop length, and k-core structure distributions.