Evolutionary of Online Social Networks Driven by Pareto Wealth Distribution and Bidirectional Preferential Attachment

TL;DR

This paper investigates the evolution of online social networks, specifically Renren, by analyzing Pareto wealth distribution and bidirectional preferential attachment, proposing a model that reproduces key evolution features and predicts degree distribution patterns.

Contribution

It introduces a novel evolutionary model based on Pareto wealth and bidirectional attachment, explaining degree distribution phenomena and aligning with empirical data from Renren.

Findings

The model accurately reproduces Renren's degree distribution.

Pareto wealth and bidirectional attachment are key to network evolution.

The model predicts the universal bowing phenomenon in degree distributions.

Abstract

Understanding of evolutionary mechanism of online social networks is greatly significant for the development of network science. However, present researches on evolutionary mechanism of online social networks are neither deep nor clear enough. In this study, we empirically showed the essential evolution characteristics of Renren online social network. From the perspective of Pareto wealth distribution and bidirectional preferential attachment, the origin of online social network evolution is analyzed and the evolution mechanism of online social networks is explained. Then a novel model is proposed to reproduce the essential evolution characteristics which are consistent with the ones of Renren online social network, and the evolutionary analytical solution to the model is presented. The model can also well predict the ordinary power-law degree distribution. In addition, the universal bowing phenomenon of the degree distribution in many online social networks is explained and predicted by the model. The results suggest that Pareto wealth distribution and bidirectional preferential attachment can play an important role in the evolution process of online social networks and can help us to understand the evolutionary origin of online social networks. The model has significant implications for dynamic simulation researches of social networks, especially in information diffusion through online communities and infection spreading in real societies.