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 …

Large Language Models (LLMs) have gained the ability to assimilate human knowledge and
facilitate natural language interactions with both humans and other LLMs. However, despite …

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

Textual graphs (TGs) are graphs whose nodes correspond to text (sentences or documents),
which are widely prevalent. The representation learning of TGs involves two stages:(i) …

Text classification is a fundamental problem in information retrieval with many real-world
applications, such as predicting the topics of online articles and the categories of e …