IEEE

A Flexible Diffusion Convolution for Graph Neural Networks

Abstract: Graph Neural Networks (GNNs) have been gaining more attention due to their excellent performance in modeling various graph-structured data. However, most of the current GNNs only consider ...
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The most graphically demanding PC games

Whenever the newest and most competent computer components are released to the public, so are the video games that fully utilize their potential. With hardware manufacturers pivoting their ...
IEEE

Multi-Kernel Collaborative Graph Convolution Neural Network for Operational Reliability Assessment Considering Varying Topologies

Abstract: Operational reliability assessment (ORA), which evaluates the risk level of power systems, is hindered by accumulated computational burdens and thus cannot meet the demands of real-time ...
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 ...