Down-Sampling Large-Scale Social Networks From Real-World Call Data
Year: 2025
Type: article
Abstract: The rapid growth of digital communication technologies has led to the generation of large-scale social networks, particularly through the analysis of call data from mobile telecommunications. However, the sheer size of these networks poses significant challenges for computational analysis and storage. This paper presents a methodology for down-sampling large-scale social networks derived from real-world call data, using the c-core decomposition technique. By applying this technique, we efficiently reduce the size of the networks while preserving critical structural properties, such as degree distribution, clustering spectrum, and community structure. The analysis is conducted on data provided by a major Asian telecommunications company, encompassing over a year of call records. We demonstrate that the c-core decomposition not only scales down the networks effectively but also maintains the essential characteristics required for meaningful social network analysis. Our results show that down-sampling using c-core decomposition provides a robust framework for analyzing and interpreting large-scale social networks, offering valuable insights into the dynamics of human communication.
Source: IEEE Access
Author: Jinho Lee
Institution: Hongik University
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,075
Funder: Hongik University
OpenAlex ID: W4411599720