Homophily principle,\ie {} nodes with the same labels or similar attributes are more likely to
be connected, has been commonly believed to be the main reason for the superiority of …

A transformer is the foundational architecture behind large language models designed to
handle sequential data by using mechanisms of self‐attention to weigh the importance of …

Graph Neural Networks (GNNs) have shown promising potential in graph representation
learning. The majority of GNNs define a local message-passing mechanism, propagating …

The emergence of large-scale pre-trained language models, such as ChatGPT, has
revolutionized various research fields in artificial intelligence. Transformersbased large …

Abstract Graph Neural Networks (GNNs) have shown great potential in the field of graph
representation learning. Standard GNNs define a local message-passing mechanism which …

Transformers are widely used deep learning architectures. Existing transformers are mostly
designed for sequences (texts or time series), images or videos, and graphs. This paper …

Designing ligand molecules that bind to specific protein binding sites is a fundamental
problem in structure-based drug design. Although deep generative models and geometric …

The design of\emph {de novo} functional proteins that bind with specific ligand molecules is
crucial in various domains like therapeutics and bio-engineering. One vital yet challenging …

Abstract Graph Transformers (GTs) have proved their advantage in graph-level tasks.
However, existing GTs still perform unsatisfactorily on the node classification task due to 1) …

Following a fast initial breakthrough in graph-based learning, Graph Neural Networks
(GNNs) have reached a widespread application in many science and engineering fields …