Tag: graph neural networks

Researchers have developed CL3AN-GNN, a graph neural network that borrows curriculum learning from education to progressively disentangle features and boost ...

A new framework called VR-FraudNet forces large language models to propose structured fraud explanations that a fixed deterministic verifier must ...

A new graph-based machine learning method called GraphiRNA models the relationships among microRNAs, including negative correlations, to diagnose Alzheimer's disease ...

A new deep learning framework called MedSegNet-AXU segments multiple sclerosis lesions from 3D MRI with a 98.58 percent Dice score ...

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

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 ...

Researchers at Central South University have developed an interpretable machine learning framework that fuses spatial and temporal process data to ...

A new graph-structured Transformer model trained with self-supervised learning and community-consensus pseudo-labeling achieves record F1-scores for detecting cryptocurrency money laundering ...

A new leakage-audited benchmark shows that random data splitting inflates toxicity model performance by up to 0.079 AUROC points while ...

In a groundbreaking advancement set to transform the landscape of drug discovery, researchers at Harvard Medical School have unveiled an ...