Benchmarking federated spatio-temporal graph neural networks against lightweight models for IoT intrusion detection: An empirical study on Edge-IIoTset

Physics-informed graph neural network surrogates for real-time transmission–distribution co-optimization

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.

UST-GNN: A unified spatial-topological graph neural network framework for urban analytics demonstrated through a case study on urban health prediction

A fault diagnosis method for rotating machinery based on spline-modulated wavelet and energy topology-guided graph convolution neural network

A spectral heterogeneous graph neural network with multi-sensor fusion for machine fault diagnosis

Exploring graph neural networks for flood modeling: Challenges, opportunities, and future prospects