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 …

Abstract Machine learning (ML), as the most promising paradigm to discover deep
knowledge from data, has been widely applied to practical applications, such as …

In recent years, graph neural networks (GNNs) have been successfully applied in many
fields due to their characteristics of neighborhood aggregation and have achieved state-of …

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 …

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 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 (GNAS) has shown promising results in automatically
designing graph neural networks. However, GNAS still requires intensive human labor with …

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 …

Heterogeneous graphs are commonly used to describe networked data with multiple types
of nodes and edges. Heterogeneous Graph Neural Networks (HGNNs) are powerful tools for …