Skip to content
Log in
#graph neural networks Open access

Temporal Graph Embeddings with 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 explicitly models the temporal dynamics of relationships within a graph. Current graph embedding techniques often treat graphs as static structures, neglecting the crucial aspect of how connections evolve over time. This work addresses this limitation by leveraging recurrent neural networks (RNNs) to process sequences of graph snapshots, learning embeddings that capture the directional, strength, and frequency changes in relationships. The core idea is to represent not just the current state of the graph, but the *process* of its evolution. We propose using weighted temporal convolution to capture nuanced changes in the graph's relational dynamics. The resulting embeddings are expected to be more informative and robust for downstream graph-based tasks compared to static embeddings.

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