Graph Neural Networks (GNNs) have made rapid developments in the recent years. Due to
their great ability in modeling graph-structured data, GNNs are vastly used in various …

Deep learning models on graphs have achieved remarkable performance in various graph
analysis tasks, eg, node classification, link prediction, and graph clustering. However, they …

Graph neural networks, a powerful deep learning tool to model graph-structured data, have
demonstrated remarkable performance on numerous graph learning tasks. To address the …

Deep neural networks (DNNs) have been widely applied to various applications, including
image classification, text generation, audio recognition, and graph data analysis. However …

Many real-world data come in the form of graphs. Graph neural networks (GNNs), a new
family of machine learning (ML) models, have been proposed to fully leverage graph data to …

In recent years, plentiful evidence illustrates that Graph Convolutional Networks (GCNs)
achieve extraordinary accomplishments on the node classification task. However, GCNs …

Unsupervised/self-supervised pre-training methods for graph representation learning have
recently attracted increasing research interests, and they are shown to be able to generalize …

Recently, a graph neural network (GNN) was proposed to analyze various graphs/networks,
which has been proven to outperform many other network analysis methods. However, it is …

Graph neural networks (GNNs) had shown excellent performance in complex graph data
modelings such as node classification, link prediction and graph classification. However …

In recent years, it has been shown that, compared to other contemporary machine learning
models, graph convolutional networks (GCNs) achieve superior performance on the node …