Xiaofei Han Publishes Groundbreaking Research on Multimodal Graph Neural Networks in Frontiers in Neuroscience

Frontiers in Neurorobotics has published a new paper by Xiaofei Han and Xin Dou introducing a next-generation artificial intelligence framework that combines graph neural networks and multimodal ...

New software toolbox enables brain-like models to learn directly from data

Researchers have developed a powerful new software toolbox that allows realistic brain models to be trained directly on data.

How To Strengthen E-Commerce Security Without Slowing Checkout

E-commerce leaders are finding new ways to fight fraud with AI, biometrics and adaptive verification—keeping checkout fast, seamless and secure.

How do Google Maps know traffic routes?

Discover how Google Maps predicts traffic routes using historical data, real-time sensors, and advanced machine learning ...
IEEE

Rotation- and Permutation-Equivariant Quantum Graph Neural Network for 3D Graph Data

Abstract: Equivariant quantum graph neural networks (EQGNNs) offer a potentially powerful method to process graph data. However, existing EQGNN models only consider the permutation symmetry of graphs, ...
C&EN

MFGNN-DSA: A Model for Predicting Drug–Side Effect Associations via Multifeature Fusion and Graph Neural Networks

Institute of Computational Medicine, School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China ...
IEEE

SAGoG: Similarity-Aware Graph of Graphs Neural Networks for Multivariate Time Series Classification

Abstract: Multivariate Time Series Classification (MTSC) has important research significance and practical value. Deep learning models have achieved considerable success in addressing MTSC problems.