Graph Neural Networks (GNNs) have achieved tremendous success in various real-world
applications due to their strong ability in graph representation learning. GNNs explore the …

Graphs are widely used to model various practical applications. In recent years, graph
convolution networks (GCNs) have attracted increasing attention due to the extension of …

Graph convolutional network (GCN) is an effective neural network model for graph
representation learning. However, standard GCN suffers from three main limitations:(1) most …

Abstract Graph Neural Networks (GNNs) have achieved tremendous success in graph
representation learning. Unfortunately, current GNNs usually rely on loading the entire …

Graph representation learning is of paramount importance for a variety of graph analytical
tasks, ranging from node classification to community detection. Recently, graph …

Graph neural networks (GNNs) have attracted an increasing attention in recent years.
However, most existing state-of-the-art graph learning methods only focus on node features …

Graph convolution networks (GCNs) are a powerful deep learning approach and have been
successfully applied to representation learning on graphs in a variety of real-world …

GCN is a widely-used representation learning method for capturing hidden features in graph
data. However, traditional GCNs suffer from the over-smoothing problem, hindering their …

An increasing number of machine learning tasks require dealing with large graph datasets,
which capture rich and complex relationship among potentially billions of elements. Graph …

It is not until recently that graph neural networks (GNNs) are adopted to perform graph
representation learning, among which, those based on the aggregation of features within the …