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D2-GCN: a graph convolutional network with dynamic disentanglement for node classification

Classic Graph Convolutional Networks (GCNs) often learn node representation holistically, which would ignore the distinct impacts from different neighbors when aggregating their features to update a ...
EurekAlert!

Dynamic traveling time forecasting based on spatial-temporal graph convolutional networks

Traveling time forecasting, the core component in GPS navigation systems and taxi-hailing apps, has attracted widespread attention. Existing research mostly focuses on independent points like traffic ...
Scientific Research Publishing

Graph Neural Networks for Anomaly Detection in Cloud Infrastructure ()

Graph Neural Networks for Anomaly Detection in Cloud Infrastructure ...
Semiconductor Engineering

Predicting Defect Properties In Semiconductors With Graph Neural Networks

A technical paper titled “Accelerating Defect Predictions in Semiconductors Using Graph Neural Networks” was published by researchers at Purdue University, Indian Institute of Technology (IIT) Madras, ...