With the continuous advancement of data science and machine learning, temporal link prediction has emerged as a crucial aspect of dynamic network analysis, providing significant research and application potential across various domains. While deep learning techniques have achieved remarkable results in temporal link prediction, most existing studies have focused on discrete model frameworks.

These frameworks face limitations in capturing deep structural features and effectively aggregating temporal information. To address these limitations, we draw inspiration from neural differential equations to propose a Continuous Temporal Graph Neural Differential Equation (CTGNDE) network model for temporal link prediction.

Specifically, we design a spatial graph Ordinary Differential Equation (ODE) to capture the spatial correlations inherent in complex spatiotemporal information. Then we employ Neural Controlled Differential Equation (Neural CDE) to learn complex evolution patterns and effectively aggregate temporal information.

Finally, we characterize completely continuous and more accurate hidden state trajectories by coupling spatial and temporal messages. Experiments conducted on 10 real-world network datasets validated the superior performance of the CTGNDE model over the state-of-the-art baselines.

L. Huang, B. Pang, Q. Yang, X. Feng, W. Wei, Link prediction by continuous spatiotemporal representation via neural differential equations, Knowledge-Based Systems 292 (2024) 111619.