HackerNoon

The Race to Build AI’s Context Layer Is Really About Meaning

Context graphs, graph memory, and ontologies for AI are converging. What does this mean for enterprise AI in 2026?

AI deciphers long-range DNA signals behind RNA splicing

Accurate RNA splicing is essential for gene expression and human health, yet predicting how DNA sequence variations affect ...
AlphaGalileo

AdapGNN: A New Framework Enhances Explainability of Graph Neural Networks for Molecular Property Prediction

Researchers have developed AdapGNN, a novel model-agnostic framework that addresses the oversmoothing problem in graph neural ...
AlphaGalileo

Crossing Virtual Boundaries: Rethinking Social Network Alignment via Graph Learning

Sub-headline: HUST researchers systematize SNA methods, building an evolutionary taxonomy based on graph representation ...
IEEE

Modularized Graph Convolutional Network

Abstract: Dear Editor, This letter presents a novel graph neural network, namely modularized graph convolution network (MGCN), to address the underexplored issue in graph convolution networks (GCNs), ...
IEEE

Graph Tensor Convolutional Network

Abstract: A dynamic graph (DG) is commonly encountered in many big data-related application scenarios, like cryptocurrency transaction analysis. A dynamic graph convolutional network (GCN) can ...
Frontiers

Efficient graph convolutional networks for seizure prediction using scalp EEG

Epilepsy is a chronic brain disease that causes persistent and severe damage to the physical and mental health of patients. Daily effective prediction of epileptic seizures is crucial for epilepsy ...
Frontiers

Spatial-Temporal Attention Mechanism and Graph Convolutional Networks for Destination Prediction

Urban transportation destination prediction is a crucial issue in the area of intelligent transportation, such as urban traffic planning and traffic congestion control. The spatial structure of the ...