GCN, a groundbreaking disentangled graph convolutional network that dynamically adjusts feature channels for enhanced node ...

Classic Graph Convolutional Networks (GCNs) often learn node representation holistically, which would ignore the distinct impacts from different ...

The Department of Agriculture’s Agriculture Training Institute seeks to boost agritourism in Calabarzon through Agri 360, a ...

What started as a one-summer program was stretched into two summers and is now being folded into Unit 5's existing summer school structure because the demand has been so great.

Discover how individual neurons in the brain learn to recognize and predict patterns in the flow of time, providing insights into the structure of time.

More information: Xiaorui Su et al, Interpretable identification of cancer genes across biological networks via transformer-powered graph representation learning, Nature Biomedical Engineering (2025).

Inspired by dynamic Graph neural networks, we propose a Group-aware Dynamic Graph Representation Learning (GDGRL) method for next POI recommendation. GDGRL connects different user sequences and ...

In an era where education stands at a crossroads between traditional methodologies and evolving student needs, teachers are ...

In this paper, we propose a novel computational method MGRL to predict drug-disease associations based on multi-graph representation learning. More specifically, MGRL first uses the graph convolution ...

Abstract: Molecule representation learning is a primary area of focus in drug discovery and molecular property prediction. In previous studies, molecules have been modeled as graphs, enabling graph ...