Abstract: Accurate and generalized collaborative prediction of multi-cluster renewable energy power generation is both an inevitable trend and urgent demand as the growth of multi-region ...

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

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 ...

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, ...

Graph representations of solid state materials that encode only interatomic-distance information lack geometrical resolution, resulting in degenerate representations that may map distinct structures ...

When we think about neural networks, especially convolutional ones, our minds often go to images—those pixelated grids where filters glide over the surface, spotting edges, shapes, and textures. But ...

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 ...

In recent years, there has been a growing prevalence of deep learning in various domains, owing to advancements in information technology and computing power. Graph neural network methods within deep ...

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 ...

Graph Convolutional Networks (GCNs) are a class of neural network architectures designed for processing and learning from data represented as graphs. A graph is a data structure that consists of nodes ...