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

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 …

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 …

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

Graph self-supervised learning (SSL), including contrastive and generative approaches,
offers great potential to address the fundamental challenge of label scarcity in real-world …

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

Large-scale graph training is a notoriously challenging problem for graph neural networks
(GNNs). Due to the nature of evolving graph structures into the training process, vanilla …

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) …

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 …