@inproceedings{
wang2024information,
title={Information Diffusion Prediction with Graph Neural Ordinary Differential Equation Network},
author={Ding Wang and Wei Zhou and Songlin Hu},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=3CkUXpwb2g}
}
Information diffusion prediction aims to forecast the path of information spreading in social networks.  Prior works generally consider the diffusion process to be driven by user correlations or preferences. Recent works focus on characterizing the dynamicity of user preferences and propose to capture users' dynamic preferences by discretizing the diffusion process into structure snapshots. Despite their effectiveness, these works summarize user preferences from partially observed structure snapshots, ignoring that users' preferences are evolving constantly. 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 \textbf{G}raph Neural \textbf{O}rdinary \textbf{D}ifferential \textbf{E}quation \textbf{N}etwork (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 dynamic and infer user dynamic preferences based on the solution of ODEs. Besides, we extract user correlations from a heterogeneous graph to complement user encoding for prediction. Finally, to predict the future user infections of the observed cascade, we represent its diffusion pattern in terms of user and temporal contexts and apply a multi-head attention module to attend to different contexts. 
Experimental results confirm our approach’s effectiveness on four real-world datasets, with our model outperforming the state-of-the-art diffusion prediction models.
Information Diffusion Prediction with Graph Neural Ordinary Differential Equation Network
Ding Wang Wei Zhou Songlin Hu