Abstract: In recent years, superpixel-based graph convolutional networks (GCNs) have drawn increasing attention within the hyperspectral image (HSI) classification community. Due to the ...
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.
Graph neural networks in Alzheimer's disease diagnosis: a review of unimodal and multimodal advances
Alzheimer's Disease (AD), a leading neurodegenerative disorder, presents significant global health challenges. Advances in graph neural networks (GNNs) offer promising tools for analyzing multimodal ...
"This is one of the most disturbing things we're seeing," an FBI official said. FBI officials say they are growing increasingly concerned about a loose network of violent predators who befriend ...
According to mathematical legend, Peter Sarnak and Noga Alon made a bet about optimal graphs in the late 1980s. They’ve now both been proved wrong. It started with a bet. In the late 1980s, at a ...
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 ...
1 School of Computer Science and Technology, Xidian University, Xi'an, China 2 Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Electronic ...
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