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 …

Recommender system is one of the most important information services on today's Internet.
Recently, graph neural networks have become the new state-of-the-art approach to …

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 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 condensation, which reduces the size of a large-scale graph by synthesizing a small-
scale condensed graph as its substitution, has immediate benefits for various graph learning …

Graph neural networks (GNNs) have emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications like …

Detecting hot social events (eg, political scandal, momentous meetings, natural hazards,
etc.) from social messages is crucial as it highlights significant happenings to help people …

Graph neural networks provide a powerful toolkit for embedding real-world graphs into low-
dimensional spaces according to specific tasks. Up to now, there have been several surveys …

The rapid growth of graph data poses significant challenges in storage, transmission, and
particularly the training of graph neural networks (GNNs). To address these challenges …

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