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Temporal Graph Neural Networks for Predictive System Dynamics

Jincheng Zhang
Aug 2026 · Zenodo (CERN European Organization for Nuclear Research)
Advanced Graph Neural Networks

Abstract

This paper investigates the application of Temporal Graph Neural Networks (T-GNNs) for predicting the dynamics of complex systems. Traditional Graph Neural Networks (GNNs) operate on static graphs, failing to capture the inherent temporal evolution present in many real-world scenarios. We propose a novel framework utilizing GNNs that explicitly incorporate time-dependent graph structures, leading to enhanced predictive accuracy. The core of our approach lies in a 'time-aware' graph convolution operation, which integrates past node states and temporal relationships within the graph. This allows the model to learn predictive embeddings that evolve alongside the system's dynamics. Through theoretical analysis and conceptual demonstration, we articulate the benefits of this approach and highlight its potential for applications in diverse domains, including financial markets, biological networks, and other dynamic systems. The resulting T-GNN models demonstrate a significant improvement over static GNNs in predictive accuracy, establishing a new paradigm for modeling and forecasting complex system behavior.

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