R-ODE: Ricci Curvature Tells When You Will be Informed

TL;DR

This paper introduces R-ODE, a novel geometric approach using Ricci curvature and ODEs to predict when users in social networks will be informed, considering the timing of information diffusion.

Contribution

It proposes the first time-aware diffusion prediction model based on Ricci curvature, integrating graph neural networks with differential equations for improved accuracy.

Findings

R-ODE outperforms existing baselines in time prediction accuracy.

The model effectively captures the dynamic diffusion process in social networks.

Ricci curvature guides message propagation along optimal paths.

Abstract

Information diffusion prediction is fundamental to understand the structure and organization of the online social networks, and plays a crucial role to blocking rumor spread, influence maximization, political propaganda, etc. So far, most existing solutions primarily predict the next user who will be informed with historical cascades, but ignore an important factor in the diffusion process - the time. Such limitation motivates us to pose the problem of the time-aware personalized information diffusion prediction for the first time, telling the time when the target user will be informed. In this paper, we address this problem from a fresh geometric perspective of Ricci curvature, and propose a novel Ricci-curvature regulated Ordinary Differential Equation (R-ODE). In the diffusion process, R-ODE considers that the inter-correlated users are organized in a dynamic system in the representation space, and the cascades give the observations sampled from the continuous realm. At each infection time, the message diffuses along the largest Ricci curvature, signifying less transportation effort. In the continuous realm, the message triggers users' movement, whose trajectory in the space is parameterized by an ODE with graph neural network. Consequently, R-ODE predicts the infection time of a target user by the movement trajectory learnt from the observations. Extensive experiments evaluate the personalized time prediction ability of R-ODE, and show R-ODE outperforms the state-of-the-art baselines.