Bi-Dynamic Graph ODE for Opinion Evolution

Modeling opinion dynamics in social networks has been the focus of multiple disciplines in recent decades. Previous studies have often modeled the opinion dynamics as a discrete and homogeneous process, neglecting its continuous and complex nature. To fill this gap, we propose a Bi-Dynamics Graph Ordinary Differential Equation (BDG-ODE) framework, which models complex opinion dynamics as the result of two dynamical processes: the evolution of positive and negative opinions. The proposed model incorporates a dual opinion encoder that processes positive and negative opinions independently. Furthermore, the temporal opinion evolution is modeled through bidirectional graph ordinary differential equations, which allows the model to capture the changes in opinion in continuous time. We introduce an opinion synthesis decoder that effectively maps the evolved representations from the latent space back to the opinion space. Extensive experiments conducted on six datasets with varying characteristics highlight the superiority of BDG-ODE in forecasting opinion evolution within social networks. It achieved an average accuracy improvement of 23.16%, an average enhancement of 29.46% in the F1 score, and an average mean square error of difference improvement of 90. 30%, and an average correlation coefficient improvement of 45.93%, significantly outperforming eight state-of-the-art models. The code for reproduction is available: https://github.com/tsinghua-fib-lab/Bi-Dynamic-Graph-ODE-for-Opinion-Evolution.