Artificial General Intelligence (AGI) has revolutionized numerous fields, yet its integration
with graph data, a cornerstone in our interconnected world, remains nascent. This paper …

Graph Neural Networks (GNNs) have evolved to understand graph structures through
recursive exchanges and aggregations among nodes. To enhance robustness, self …

Large Language Models (LLMs) have revolutionized the fields of computer vision (CV) and
natural language processing (NLP). One of the most notable advancements of LLMs is that a …

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

As the deep learning revolution marches on, self-supervised learning has garnered
increasing attention in recent years thanks to its remarkable representation learning ability …

Masked graph modeling excels in the self-supervised representation learning of molecular
graphs. Scrutinizing previous studies, we can reveal a common scheme consisting of three …

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

Self-supervised learning with masked autoencoders has recently gained popularity for its
ability to produce effective image or textual representations, which can be applied to various …

Foundation models (FMs) currently dominate news headlines. They employ advanced deep
learning architectures to extract structural information autonomously from vast datasets …

The generation of large-scale urban layouts has garnered substantial interest across various
disciplines. Prior methods have utilized procedural generation requiring manual rule coding …