A leakage-resistant graph learning framework separates predecision risk warning from context-aware diagnosis in construction supply networks and gates resource allocation ...
A hybrid framework combining graph diffusion convolution with GraphSAGE improves inductive link prediction across diverse benchmark networks by capturing long-range ...
Researchers developed a topology-aware point-cloud generation framework combining simplicial diffusion and multi-scale graph decoding to preserve global structural coherence without ...
A large-scale study of more than 150,000 node embeddings finds that dimensionality substantially affects embedding stability, with effects that depend ...
The MARLIN framework learns delay prediction and routing from five raw IoT sensor streams, but its benchmark uses synthetic labels ...
A multi-agent AI system combining large language models, knowledge graph embeddings, and collaborative agents reads power grid alarm rules, recognizes ...
Researchers in India have built a hybrid graph neural network framework that converts retinal images into graph structures, achieving up ...
A hybrid AI model that reasons about park spaces as interconnected graphs before generating images produces urban green space layouts ...
Researchers in Cyprus used classical machine learning and GraphSAGE-based graph representation learning to predict missing transmission links in COVID-19 infection ...
A new deep learning framework called MedSegNet-AXU segments multiple sclerosis lesions from 3D MRI with a 98.58 percent Dice score ...
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