The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

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 …

The advent of the sixth generation of mobile communications (6G) ushers in an era of
heightened demand for advanced network intelligence to tackle the challenges of an …

In the past decade, advances in deep learning have resulted in breakthroughs in a variety of
areas, including computer vision, natural language understanding, speech recognition, and …

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 …

Recent years have witnessed the popularity and success of graph neural networks (GNN) in
various scenarios. To obtain data-specific GNN architectures, researchers turn to neural …

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 (NAS) has gained popularity in automatically designing
powerful graph neural networks (GNNs) with relieving human efforts. However, existing …

Continual graph learning is rapidly emerging as an important role in a variety of real-world
applications such as online product recommendation systems and social media. While …