Tag: Graph Neural Networks

A UCLA mathematician has introduced a distance-based representation learning framework grounded in coarse geometry that reads the global shape of ...

Researchers at Shenyang University of Technology have developed a distance-aware adaptive graph neural network that estimates 6D object poses from ...

Researchers have developed E-GTNet, a hybrid graph neural network that detects and visualizes evolving illicit Bitcoin transaction networks using edge-aware ...

Researchers have developed BHyGNN+, a self-supervised framework that learns representations of heterophilic hypergraphs without labels by contrasting each hypergraph against ...

A new framework combines explainable graph neural networks with bio-inspired repair algorithms to detect, diagnose, and autonomously heal faults in ...

A new graph convolution Transformer framework with self-distillation improves multi-view graph clustering by suppressing cross-view noise and exploiting complementary information ...

A new framework called LLM-H2G fuses large language model semantics with hypergraph contrastive learning to predict herb–disease associations, outperforming existing ...

A sweeping review details how graph neural networks, generative AI and autonomous laboratories are accelerating the discovery of cathode, anode ...

A new dual-masked graph autoencoder called DM-GAE maps mineral prospectivity in Tibet with high accuracy despite scarce labeled deposits, outperforming ...

Researchers have developed DePass, a dual-enhanced graph learning framework that iteratively denoises and integrates paired multi-omics data across single-cell and ...