2017/05/23 by Malay Chakrabarti, Lenwood S. Heath, Chakrabarti, Malay +3
Computer Science · Physics and Astronomy · #Advanced Clustering Algorithms Research #Complex Network Analysis Techniques #Data Visualization and Analytics #FOS: Computer and information sciences #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.1705.08473
openalex publication_date 2017/05/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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. The key technical issue is to be able to generate synthetic topologies with pre-specified, 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 attenuation 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 coefficient, hop length, and k-core structure distributions.