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 ...
Game Rant

Most Graphically Demanding Games In 2026 That Will Melt Your RTX 5080

Vlad Mazanko is Ukraine-based gaming enthusiast, writing about the industry since 2013 and covering everything from games and studios to movies and TV shows. He joined the Valnet family back in 2021, ...
IEEE

From Cluster Assumption to Graph Convolution: Graph-Based Semi-Supervised Learning Revisited

Abstract: Graph-based semi-supervised learning (GSSL) has long been a research focus. Traditional methods are generally shallow learners, based on the cluster assumption. Recently, graph convolutional ...
Frontiers

Dual-channel deep graph convolutional neural networks

The dual-channel graph convolutional neural networks based on hybrid features jointly model the different features of networks, so that the features can learn each other and improve the performance of ...
IEEE

Contrastive Graph Convolution Network With Skip Connections for Few-Shot Hyperspectral Image Classification

Abstract: Graph convolution networks (GCNs) have achieved impressive results for few-shot hyperspectral image (HSI) classification. However, current methods focus on migrating labels from support ...
GitHub

DiffKG: Knowledge Graph Diffusion Model for Recommendation

This is the PyTorch implementation for DiffKG proposed in the paper DiffKG: Knowledge Graph Diffusion Model for Recommendation, which is accepted by WSDM 2024 Oral. Yangqin Jiang, Yuhao Yang, Lianghao ...