Self-supervised learning on graph-structured data has drawn recent interest for learning
generalizable, transferable and robust representations from unlabeled graphs. Among …

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

Self-supervision is recently surging at its new frontier of graph learning. It facilitates graph
representations beneficial to downstream tasks; but its success could hinge on domain …

Graph data augmentations have demonstrated remarkable performance on homophilic
graph neural networks (GNNs). Nevertheless, when transferred to a heterophilic graph …

Graph Neural Network (GNN) is an effective model for processing graph-structured data.
Most GNNs are designed to solve homophilic graphs, where all nodes belong to the same …

Three-dimensional model is an important form of human cognition, and with the
development of computer technology, 3D reconstruction technology has important …

Graph Neural Networks (GNNs) have achieved tremendous success in a variety of real-
world applications by relying on the fixed graph data as input. However, the initial input …

Graph Convolutional Networks (GCNs) are widely used in many applications yet still need
large amounts of labelled data for training. Besides, the adjacency matrix of GCNs is stable …