Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

In academia and industries, graph neural networks (GNNs) have emerged as a powerful
approach to graph data processing ranging from node classification and link prediction tasks …

Graph neural networks (GNNs) have been widely used in various graph analysis tasks. As
the graph characteristics vary significantly in real-world systems, given a specific scenario …

The existing graph neural architecture search (GNAS) methods heavily rely on supervised
labels during the search process, failing to handle ubiquitous scenarios where supervisions …

Graph neural networks (GNNs) have achieved state-of-the-art performance in various graph-
based tasks. However, as mainstream GNNs are designed based on the neural message …

Machine learning on graphs has been extensively studied in both academic and industry.
However, as the literature on graph learning booms with a vast number of emerging …

Graph classification is an important problem with applications across many domains, like
chemistry and bioinformatics, for which graph neural networks (GNNs) have been state-of …

Graph neural architecture search (GraphNAS) has recently aroused considerable attention
in both academia and industry. However, two key challenges seriously hinder the further …

Graph neural architecture search (GraphNAS) has shown great potential for automatically
designing graph neural architectures for graph related tasks. However, multi-task GraphNAS …

There has been a lot of activity in graph representation learning in recent years. Graph
representation learning aims to produce graph representation vectors to represent the …