Tech Xplore

Shortest paths research narrows a 25-year gap in graph algorithms

Most of you have used a navigation app like Google Maps for your travels at some point. These apps rely on algorithms that ...
IEEE

A Convolution Bias-Incorporated Nonnegative Latent Factorization of Tensors Model for Accurate Representation Learning to Dynamic Directed Graphs

Abstract: A dynamic directed graph (DDG) can describe complex dynamic interactions among massive entities, for example, traffic transmissions in a metropolitan area network (MAN), in a natural way.
IEEE

A Nonlinear Weighted Graph Convolution Network Based on Manifold Geometric Regularization for Hyperspectral Image Classification

Abstract: Extracting spatial–spectral joint features has become a critical approach for improving model classification performance in the field of hyperspectral image classification (HSIC). However, ...
Frontiers

Association prediction of lncRNAs and diseases using multiview graph convolution neural network

Long noncoding RNAs (lncRNAs) regulate physiological processes via interactions with macromolecules such as miRNAs, proteins, and genes, forming disease-associated regulatory networks. However, ...
Oak Ridge National Lab

Impedance-Aware Graph Convolutional Networks for Voltage Estimation in Active Distribution Networks

Voltage estimation plays a key role in ensuring the effective control and reliability of distribution networks. However, traditional machine learning methods often fail to capture the details of the ...
Microsoft

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties.

The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input ...