Recently, techniques for applying convolutional neural networks to graph-structured data
have emerged. Graph convolutional neural networks (GCNNs) have been used to address …

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

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

Graph based semi-supervised learning (SSL) methods play an increasingly important role in
practical machine learning systems. A crucial step in graph based SSL methods is the …

We propose a data-efficient Gaussian process-based Bayesian approach to the semi-
supervised learning problem on graphs. The proposed model shows extremely competitive …

Graph convolutional networks (GCN) have achieved promising performance in attributed
graph clustering and semi-supervised node classification because it is capable of modeling …

Self-supervised learning is currently gaining a lot of attention, as it allows neural networks to
learn robust representations from large quantities of unlabeled data. Additionally, multi-task …

We present graph partition neural networks (GPNN), an extension of graph neural networks
(GNNs) able to handle extremely large graphs. GPNNs alternate between locally …

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 …