The investigation of social networks based on multi-component random graphs

TL;DR

This paper develops calibration methods for social network models using non-homogeneous random graphs, enabling better prediction and control of network development through mathematical foundations and computer experiments.

Contribution

It introduces new calibration techniques for social network models based on non-homogeneous random graphs, integrating nonlinear preferential attachment and Erdos-Renyi theories.

Findings

Effective calibration of social network models achieved

Computer experiments validate the models

Enhanced prediction of network development

Abstract

The methods of non-homogeneous random graphs calibration are developed for social networks simulation. The graphs are calibrated by the degree distributions of the vertices and the edges. The mathematical foundation of the methods is formed by the theory of random graphs with the nonlinear preferential attachment rule and the theory of Erdos-Renyi random graphs. In fact, well-calibrated network graph models and computer experiments with these models would help developers (owners) of the networks to predict their development correctly and to choose effective strategies for controlling network projects.