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 …

The emergence of large-scale pre-trained language models has revolutionized various AI
research domains. Transformers-based Large Language Models (LLMs) have gradually …

Foundation models have emerged as critical components in a variety of artificial intelligence
applications, and showcase significant success in natural language processing and several …

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 …

Graph Neural Networks (GNNs) have empowered the advance in graph-structured data
analysis. Recently, the rise of Large Language Models (LLMs) like GPT-4 has heralded a …

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

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 …

Recently, large language models (LLMs) have demonstrated superior capabilities in
understanding and zero-shot learning on textual data, promising significant advances for …