Information Diffusion Prediction with Graph Neural Ordinary Differential Equation Network

Information diffusion prediction aims to forecast the path of information spreading in social networks by exploiting user correlations or preferences. Recent works focus on characterizing the dynamic of user preferences and propose to capture users' dynamic preferences by discretizing the diffusion process into structure snapshots. Despite their effectiveness, these works simply summarize users' dynamic preferences from partially observed structure snapshots, ignoring the continuous evolution of the preferences. Moreover, discretizing the diffusion process makes these models overlook abundant structure information across different periods, reducing their ability to discover potential participants. To address the above issues, we propose a novel Graph Neural Ordinary Differential Equation Network (GODEN) for information diffusion prediction, which incorporates neural ordinary differential equations (ODE) to model the continuous dynamics of the diffusion process. Specifically, we design two coupled ODE functions on nodes and edges to describe their co-evolution dynamics and infer users' dynamic preferences based on the solution of ODEs. To predict the future infections of the observed cascade, we represent its diffusion pattern in terms of temporal and user contexts and apply a multi-head attention module to attend to different contexts. Experimental results confirm our approach's effectiveness, with our model outperforming the state-of-the-art diffusion prediction models.