We study the problem of semi-supervised learning on graphs, for which graph neural
networks (GNNs) have been extensively explored. However, most existing GNNs inherently …

Graph neural networks (GNNs) have been widely adopted for semi-supervised learning on
graphs. A recent study shows that the graph random neural network (GRAND) model can …

Abstract Graph Neural Networks (GNNs) have achieved remarkable performance in the task
of the semi-supervised node classification. However, most existing models learn a …

Many interesting problems in machine learning are being revisited with new deep learning
tools. For graph-based semi-supervised learning, a recent important development is graph …

Abstract Graph Convolutional Networks (GCNs) have shown significant improvements in
semi-supervised learning on graph-structured data. Concurrently, unsupervised learning of …

Graph-based algorithms are among the most successful paradigms for solving semi-
supervised learning tasks. Recent work on graph convolutional networks and neural graph …

Recently popularized graph neural networks achieve the state-of-the-art accuracy on a
number of standard benchmark datasets for graph-based semi-supervised learning …

Recently, graph convolutional networks (GCN) have made substantial progress in semi-
supervised learning (SSL). However, established GCN-based methods have two major …

Graph-based methods have been demonstrated as one of the most effective approaches for
semi-supervised learning, as they can exploit the connectivity patterns between labeled and …

Graph Neural Networks (GNNs) show promising results for semi-supervised learning tasks
on graphs, which become favorable comparing with other approaches. However, similar to …