Understanding Correlated Information Diffusion: From a Graphical Evolutionary Game Perspective

Abstract

In online social networks, millions of connected intelligent individuals actively interact with each other, which not only facilitates opinion sharing but also offers the platform to spread detrimental gossips and rumors. Therefore, it is of crucial importance to better understand how the avalanche of information propagates over social networks and affects our social life and economy. However, most model-based works on information diffusion either consider the spreading of one single message or assume that different information spreads independently. In this letter, we investigate how correlated information spreads together and jointly influences users' decisions from a graphical evolutionary game perspective. We model the multi-source information diffusion process, analyze the impact of information's correlation and time delay on the evolutionary dynamics and the evolutionary stable states (ESS). Simulation results on synthetic networks and Facebook real-world networks are consistent with our analytical results. This investigation offers important insights to the understanding and management of multi-source information diffusion.