Graph neural networks have emerged as a leading architecture for many graph-level tasks,
such as graph classification and graph generation. As an essential component of the …

Graph neural networks provide a powerful toolkit for embedding real-world graphs into low-
dimensional spaces according to specific tasks. Up to now, there have been several surveys …

Graph convolutional network (GCN) with the powerful capacity to explore graph-structural
data has gained noticeable success in recent years. Nonetheless, most of the existing GCN …

Graph classification is an important problem with applications across many domains, for
which graph neural networks (GNNs) have been state-of-the-art (SOTA) methods. In the …

Graph neural networks (GNNs) have been widely applied in the recommendation tasks and
have achieved very appealing performance. However, most GNN-based recommendation …

Graph convolutional networks (GCNs) have shown superior performance on graph
classification tasks, and their structure can be considered as an encoder–decoder pair …

Over-squashing and over-smoothing are two critical issues, that limit the capabilities of
graph neural networks (GNNs). While over-smoothing eliminates the differences between …

Abstract Graph Neural Networks (GNNs) have been successfully applied to graph-level
tasks in various fields such as biology, social networks, computer vision, and natural …

Traffic supply-demand mismatching has a severe impact on intelligent transportation
systems. Fortunately, order dispatching is a promising option to mitigate the traffic supply …

Graph representations in fixed dimensional feature space are vital in applying learning tools
and data mining algorithms to perform graph analytics. Such representations must encode …