graph neural networks

Quantum-inspired scenario-adaptive differential privacy with hybrid attention–residual deep learning for utility-aware urban location analytics

A Quantum-Inspired Scenario-Adaptive Differential Privacy framework that combines k-nearest-neighbor (kNN)-based adaptive privacy budgeting with structured quantum-inspired noise generation and a Hybrid Attention–Residual Deep Neural Network for mechanism selection demonstrates that structured quantum-inspired perturba...

Wafer-scale SOT-MRAM for analog crossbar array applications

Analog crossbar arrays consisting of emerging memory devices can alleviate the computational strain required by vector matrix multiplications for neural network applications. The ability to produce spin orbit torque-magnetic random-access memory (SOT-MRAM) at wafer-scale positions SOT-MRAM as a strong memory candidate....

QUBO-Compatible Active Learning for Inverse Design of High-Entropy Alloys

A QUBO-compatible active-learning framework for inverse design of high-entropy alloys using a pretrained graph-neural-network predictor as a fixed property oracle and the learned quadratic surrogate can be exported directly as a QUBO.

A multimodal EEG-based psychological risk representation method using spatiotemporal graph attention and adaptive gated fusion

A cross-modal temporal representation method that integrates spatiotemporal graph attention with an adaptive gating mechanism that significantly improves the accuracy and robustness of risk prediction on the AMIGOS dataset, effectively overcoming the subjective interference of questionnaire-based assessments.

Modeling of a packed pebble cell on coupled conduction–radiation–convection heat transfer: A physics-embedded graph neural network

Physics-Aware Recurrent and Graphical Learning for Robust Distribution System State Estimation Across Unseen Networks

Robust distribution system state estimation (DSSE) is increasingly vital in modern, complex networks. However, conventional optimization-based DSSE approaches often face challenges with real-time complexity. Consequently, recent advancements have introduced learning-based, physics-aware DSSE methods that leverage netwo...

Joint dynamic pruning method for graph neural networks under traffic prediction scenarios

Graph Theory, Graph Neural Networks, and Reinforcement Learning in marine ecology: A systematic review of methodologies, theoretical rigour, and future directions

Orbital-geometric atomic line graph neural network-VoltaDelta framework for assisted screening of zinc-ion battery cathode materials

MF-HGNN: A multi-view fusion heterogeneous graph neural network for psychiatric disorder diagnosis

A dynamic spatio-temporal graph neural network adaptive to operating conditions: Learning evolutionary correlations in multi-sensor data for aero-engine

Topology-aware graph neural network for fusing sparse sensor data with finite element predictions in structural random vibration assessment