Deep semi-supervised learning is a fast-growing field with a range of practical applications.
This paper provides a comprehensive survey on both fundamentals and recent advances in …

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 …

Collaborative Filtering (CF) has emerged as fundamental paradigms for parameterizing
users and items into latent representation space, with their correlative patterns from …

Node features of graph neural networks (GNNs) tend to become more similar with the
increase of the network depth. This effect is known as over-smoothing, which we …

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 …

This work develops mixup for graph data. Mixup has shown superiority in improving the
generalization and robustness of neural networks by interpolating features and labels …

Transformer architectures have become the model of choice in natural language processing
and are now being introduced into computer vision tasks such as image classification, object …

Deep graph neural networks (GNNs) have achieved excellent results on various tasks on
increasingly large graph datasets with millions of nodes and edges. However, memory …

Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler
of its great success is the availability of abundant and high-quality data for building machine …

In the last few years, graph neural networks (GNNs) have become the standard toolkit for
analyzing and learning from data on graphs. This emerging field has witnessed an extensive …