Abstract Graph Neural Networks (GNNs, in short) are a powerful computational tool to jointly
learn graph structure and node/edge features. They achieved an unprecedented accuracy in …

We consider the graph link prediction task, which is a classic graph analytical problem with
many real-world applications. With the advances of deep learning, current link prediction …

Graph neural networks (GNNs) have achieved great success in recent years. Three most
common applications include node classification, link prediction, and graph classification …

Deep models can be made scale-invariant when trained with multi-scale information.
Images can be easily made multi-scale, given their grid-like structures. Extending this to …

Link prediction is an important application of graph neural networks. By predicting missing or
future links between pairs of nodes, link prediction is widely used in social networks, citation …

Graph neural networks achieve high accuracy in link prediction by jointly leveraging graph
topology and node attributes. Topology, however, is represented indirectly; state-of-the-art …

Link prediction, as a fundamental task for graph neural networks (GNNs), has boasted
significant progress in varied domains. Its success is typically influenced by the expressive …

It is well known that the graph classification performance of graph neural networks often
improves by adding an artificial virtual node to the graphs, which is connected to all graph …

Link prediction is a crucial problem in graph-structured data. Due to the recent success of
graph neural networks (GNNs), a variety of GNN-based models were proposed to tackle the …

Abstract Graph Neural Networks (GNNs) have been widely applied to various fields for
learning over graph-structured data. They have shown significant improvements over …