Large models have emerged as the most recent groundbreaking achievements in artificial
intelligence, and particularly machine learning. However, when it comes to graphs, large …

Vector quantization, renowned for its unparalleled feature compression capabilities, has
been a prominent topic in signal processing and machine learning research for several …

Large models have emerged as the most recent groundbreaking achievements in artificial
intelligence, and particularly machine learning. However, when it comes to graphs, large …

Graph Neural Networks (GNNs) have demonstrated effectiveness in various graph-based
tasks. However, their inefficiency in training and inference presents challenges for scaling …

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 …

We present a novel graph tokenization framework that generates structure-aware, semantic
node identifiers (IDs) in the form of a short sequence of discrete codes, serving as symbolic …

Despite recent community revelations about the advancements and potential of Large
Language Models (LLMs) in understanding Text-Attributed Graphs (TAG), the deployment of …

Abstract While Graph Neural Networks (GNNs) have demonstrated their effectiveness in
processing non-Euclidean structured data, the neighborhood fetching of GNNs is time …

Knowledge distillation (KD), as an effective compression technology, is used to reduce the
resource consumption of graph neural networks (GNNs) and facilitate their deployment on …

Representation learning and\emph {de novo} generation of proteins are pivotal
computational biology tasks. Whilst natural language processing (NLP) techniques have …