Large language models (LLMs), such as GPT4 and LLaMA, are creating significant
advancements in natural language processing, due to their strong text encoding/decoding …

Graph plays a significant role in representing and analyzing complex relationships in real-
world applications such as citation networks, social networks, and biological data. Recently …

Learning on Graphs has attracted immense attention due to its wide real-world applications.
The most popular pipeline for learning on graphs with textual node attributes primarily relies …

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

Graphs are an essential data structure utilized to represent relationships in real-world
scenarios. Prior research has established that Graph Neural Networks (GNNs) deliver …

Representation learning on text-attributed graphs (TAGs) has become a critical research
problem in recent years. A typical example of a TAG is a paper citation graph, where the text …

Large language models (LLMs), while exhibiting exceptional performance, suffer from
hallucinations, especially on knowledge-intensive tasks. Existing works propose to augment …

With the development of foundation models such as large language models, zero-shot
transfer learning has become increasingly significant. This is highlighted by the generative …

Graph plays a significant role in representing and analyzing complex relationships in real-
world applications such as citation networks, social networks, and biological data. Graph …

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