Tag: Graph Neural Networks

A controlled study of synthetic identity fraud detection shows that rule-anchored heterogeneous graph models win through complete identity relation structure ...

A new density functional theory study shows that iron-centered nitrogen-doped carbon coordination frameworks bind the chemotherapy drug 5-fluorouracil far more ...

Researchers in India have developed a graph neural network framework called TADGLN-LSTM that quantifies how individual scientists' research topics drift ...

Researchers have built a five-branch hybrid deep learning framework called ADAPT-FUSE that predicts cross-border e-commerce delivery delays with competitive accuracy ...

A new frequency-guided adaptive graph network separates financial risk contagion into energy-balanced spectral bands to predict market dynamics more accurately ...

A new benchmark study shows that a simple last-year baseline outperformed sophisticated graph neural networks in forecasting annual vegetation productivity ...

A new critical review argues that topological deep learning, spanning persistent homology, simplicial and cellular complexes, and sheaf-based models, is ...

Researchers have developed DCM-Net, a dual conditional modulation graph attention network that adapts both its input features and its own ...

Researchers at China Jiliang University have combined fine-tuned large language models with graph attention networks to complete incomplete knowledge graphs ...

Researchers have developed DIRA, a model that combines dynamic entity embeddings with implicit relation-aware self-attention to significantly improve temporal knowledge ...