Recent years have witnessed fast developments of graph neural networks (GNNs) that have
benefited myriads of graph analytic tasks and applications. In general, most GNNs depend …

Deep models trained in supervised mode have achieved remarkable success on a variety of
tasks. When labeled samples are limited, self-supervised learning (SSL) is emerging as a …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Many widely used datasets for graph machine learning tasks have generally been
homophilous, where nodes with similar labels connect to each other. Recently, new Graph …

Deep learning on graphs has attracted significant interests recently. However, most of the
works have focused on (semi-) supervised learning, resulting in shortcomings including …

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 …

As explanations are increasingly used to understand the behavior of graph neural networks
(GNNs), evaluating the quality and reliability of GNN explanations is crucial. However …

A cursory reading of the literature suggests that we have made a lot of progress in designing
effective adversarial defenses for Graph Neural Networks (GNNs). Yet, the standard …

Abstract Graph Neural Networks (GNNs) are increasingly important given their popularity
and the diversity of applications. Yet, existing studies of their vulnerability to adversarial …

The graph Transformer emerges as a new architecture and has shown superior
performance on various graph mining tasks. In this work, we observe that existing graph …