graph neural networks

A Parallel Convolutional–Hypergraph Feature learning Network for pulmonary nodule classification

Revisiting topological bias in imbalanced graph learning: A Dual-Balanced Graph Neural Network approach

Learning Calibrated and Transferable Node-Level Infection Risk in Stochastic Epidemics on Complex Networks

基于动态拓扑记忆网络的神经符号推理

This paper proposes a novel approach to neural-symbolic reasoning by introducing a Dynamic Topological Memory Network (DTMN). The core idea is to construct a memory network capable of dynamically adjusting its internal topology to mirror the complexity and relationships within the input data. This addresses a key limit...

Intelligent Transportation Systems: A Review of Integration of Digital Twin and Machine Learning Control

This paper presents a comprehensive review of the integration of Intelligent Transportation Systems (ITS) and Digital Twin (DT) technologies for intelligent traffic management, and identifies key research gaps and outlines future research directions toward scalable, reliable, adaptive, and real-time DT-enabled ITS arch...

A Lightweight Explainable AI Framework for Early Brain Tumor Detection Using EEG Functional Connectivity

The findings support the feasibility of lightweight graph-based EEG analysis as an auxiliary screening and decision-support approach and at the same time, MRI and CT remain necessary for clinical confirmation and anatomical characterization.

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...