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 …

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 …

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 …

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 …

In this paper, we propose a next-generation link prediction method, Weisfeiler-Lehman
Neural Machine (WLNM), which learns topological features in the form of graph patterns that …

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

Graph neural networks (GNNs) have received remarkable success in link prediction
(GNNLP) tasks. Existing efforts first predefine the subgraph for the whole dataset and then …

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

Link prediction attempts to predict whether an unseen edge exists based on only a portion of
the graph. A flurry of methods has been created in recent years that attempt to make use of …

Both node classification and link prediction are popular topics of supervised learning on the
graph data, but previous works seldom integrate them together to capture their …