Emerging as fundamental building blocks for diverse artificial intelligence applications,
foundation models have achieved notable success across natural language processing and …

The history of artificial intelligence (AI) has witnessed the significant impact of high-quality
data on various deep learning models, such as ImageNet for AlexNet and ResNet. Recently …

Graph structures have attracted much research attention for carrying complex relational
information. Based on graphs, many algorithms and tools are proposed and developed for …

A fundamental challenge of bipartite graph representation learning is how to extract
informative node embeddings. Self-Supervised Learning (SSL) is a promising paradigm to …

Graph machine learning has been extensively studied in both academia and industry.
However, in the literature, most existing graph machine learning models are designed to …

Graph self-supervised learning has attracted plenty of attention in recent years. However,
most existing methods are designed for homogeneous graphs yet not tailored for bipartite …

In the big data era, the relationship between entries becomes more and more complex.
Many graph (or network) algorithms have already paid attention to dynamic networks, which …

Despite the success of the carefully-annotated benchmarks, the effectiveness of existing
graph neural networks (GNNs) can be considerably impaired in practice when the real-world …

In the era of big data and Artificial Intelligence, an emerging paradigm is to utilize contrastive
self-supervised learning to model large-scale heterogeneous data. Many existing foundation …

Conditional graph generation is crucial and challenging since the conditional distribution of
graph topology and feature is complicated and the semantic information is hard to capture …