Evolutionary Information Diffusion over Social Networks

TL;DR

This paper introduces an evolutionary game theoretic framework to model and analyze information diffusion in large-scale social networks, considering user decisions and socio-economic factors, validated through experiments on synthetic and real-world data.

Contribution

It develops a novel game theoretic model for information diffusion that accounts for user behavior and network heterogeneity, providing closed-form stable states and empirical validation.

Findings

The framework accurately predicts information spread patterns.

Experimental results confirm the model's effectiveness on real social networks.

The approach captures the influence of user decisions on diffusion dynamics.

Abstract

Social networks have become ubiquitous in our daily life, as such it has attracted great research interests recently. A key challenge is that it is of extremely large-scale with tremendous information flow, creating the phenomenon of "Big Data". Under such a circumstance, understanding information diffusion over social networks has become an important research issue. Most of the existing works on information diffusion analysis are based on either network structure modeling or empirical approach with dataset mining. However, the information diffusion is also heavily influenced by network users' decisions, actions and their socio-economic connections, which is generally ignored in existing works. In this paper, we propose an evolutionary game theoretic framework to model the dynamic information diffusion process in social networks. Specifically, we analyze the framework in uniform degree and non-uniform degree networks and derive the closed-form expressions of the evolutionary stable network states. Moreover, the information diffusion over two special networks, Erd\H{o}s-R\'enyi random network and the Barab\'asi-Albert scale-free network, are also highlighted. To verify our theoretical analysis, we conduct experiments by using both synthetic networks and real-world Facebook network, as well as real-world information spreading dataset of Twitter and Memetracker. Experiments shows that the proposed game theoretic framework is effective and practical in modeling the social network users' information forwarding behaviors.