Skip to content
Log in
#graph neural networks Open access

Temporal Graph Embedding via Relational Dynamics

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

Abstract

This paper introduces a novel approach to graph embedding that leverages the temporal dynamics of graph relationships. Traditional graph embedding techniques often treat graphs as static structures, failing to capture the evolving nature of connections between nodes. Our method, Temporal Graph Embedding via Relational Dynamics (TGERD), employs recurrent neural networks (RNNs) to model the sequence of changes in relationships, effectively learning representations of nodes based on their temporal evolution. The core innovation lies in a weighted temporal graph convolution, where recent relationship changes are given higher priority, allowing the model to adapt to shifts in the graph's structure over time. We demonstrate that this approach significantly improves graph embedding quality compared to static embedding methods, particularly in scenarios where relationships are not static. The algorithm is designed for flexibility and can be applied to various graph datasets with evolving connections.

DOI
View source

Similar papers

#graph neural networks Oct 2026

Locality-Preserving Graph Laplacian Manifold Learning Based Model Predictive Control for Three-Phase Inverters

This article presents a model predictive control (MPC) strategy for three-phase inverters based on locality preserving projections (LPPs). Unlike conventional machine learning–based MPC approaches that rely on predefined or high-dimensional input features, the proposed LPP-MPC automatically extracts compact, informative representations by preserving the data’s intrinsic geometric structure. This dimensionality reduction enables fast linear control-law evaluation with computational complexity O(1), making the controller well-suited for real-time implementation. Experimental results demonstrate that the LPP-MPC achieves lower total harmonic distortion (THD) and reduced tracking error compared to quadratic-programming MPC under both linear and nonlinear load conditions, and the proposed controller maintains consistently lower THD throughout load transients than other methods such as two-degree-of-freedom MPC. Compared to existing model-free MPC and deep learning neural network, the LPP-MPC has the lowest THD and root mean square error with the least computational time owing to its efficient linear structure and strong generalization capability.

Jianwu Zeng, Lizheng Cheng, V. Winstead et al. · 1 citation

Related blog posts