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 …

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

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 …

In the era of big data and Artificial Intelligence, an emerging paradigm is to utilize contrastive
self-supervised learning to model large-scale heterogeneous data. Many existing foundation …

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 …

Graph plays an important role in representing complex relationships in real-world
applications such as social networks, biological data and citation networks. In recent years …

Retrieval-Augmented Generation (RAG) systems enhance large language models (LLMs)
by integrating external knowledge sources, enabling more accurate and contextually …

The integration of Large Language Models (LLMs) with Graph Representation Learning
(GRL) marks a significant evolution in analyzing complex data structures. This collaboration …