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 networks ...

In recent years, Graph Neural Networks (GNNs) have achieved significant success in graph-based tasks. However, they still face challenges in complex scenarios, particularly in integrating local and ...

The latest advance in recommendation shows that better user and item representations can be learned via performing graph convolutions on the user-item interaction graph. However, such finding is ...

This repository contains the official implementation of our paper: Hierarchical Convolution and Graph Net for Utilizing Structural Information of Image Z. Li, D. Dai and T. Yi 2024 7th International ...

Pinterest today shared details about how it created PinSage, a graph convolutional network that can learn about things like nearby Pins, or nodes, in massive web-scale graphs.