Tag: Graph Neural Networks

Researchers at Fuzhou University have developed RCGELM-AE, a graph embedding model that trains in closed form without gradient descent while ...

Researchers have developed SURGE, a graph unlearning framework that treats data deletion requests as structural perturbations and repairs graph neural ...

Researchers in China have developed an interpretable multimodal AI framework that predicts the research octane number of pure compounds and ...

A systematic review in Applied Intelligence introduces a two-axis taxonomy that maps how large language models enhance graph neural networks ...

A new decoupled graph-augmented transformer architecture separates static road topology from dynamic traffic patterns to reduce optimization interference and achieve ...

Researchers in India have built a four-part neural system that uses contrastive learning, graph refinement, and reinforcement learning to find ...

A new framework pairs momentum contrastive learning with the GATv2 graph attention network to detect rare, legally entangled financial credit ...

A new study combines graph neural networks with explainable AI to map how Laotian migrant workers in Bangkok build and ...

A new review in the Journal of Materials Science maps how machine learning, from graph neural networks to large language ...

Researchers have developed IGA, an unsupervised graph alignment framework that uses isomorphism-aware neural networks to predict anchor links across social ...