Recently, Retrieval-Augmented Generation (RAG) has achieved remarkable success in
addressing the challenges of Large Language Models (LLMs) without necessitating …

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

The emergence of large language models (LLMs) has revolutionized the way we interact
with graphs, leading to a new paradigm called GraphLLM. Despite the rapid development of …

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

Large language models (LLMs) cannot see or understand graphs. The current Graph LLM
method transform graph structures into a format LLMs understands, utilizing LLM as a …

Graphs play an important role in representing complex relationships in various domains like
social networks, knowledge graphs, and molecular discovery. With the advent of deep …

Text-Attributed Graphs (TAGs) are graphs of connected textual documents. Graph models
can efficiently learn TAGs, but their training heavily relies on human-annotated labels, which …

The growing ubiquity of relational data structured as graphs has underscored the need for
graph learning models with exceptional generalization capabilities. However, current …

Foundation models, such as Large Language Models (LLMs) or Large Vision Models
(LVMs), have emerged as one of the most powerful tools in the respective fields. However …

Recently, research on Text-Attributed Graphs (TAGs) has gained significant attention due to
the prevalence of free-text node features in real-world applications and the advancements in …