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 …

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 …

Do current large language models (LLMs) better solve graph reasoning and generation
tasks with parameter updates? In this paper, we propose InstructGraph, a framework that …

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 …

Large models have emerged as the most recent groundbreaking achievements in artificial
intelligence, and particularly machine learning. However, when it comes to graphs, large …

Graph neural networks (GNNs) have emerged as a powerful tool for effectively mining and
learning from graph-structured data, with applications spanning numerous domains …

Large language models (LLMs) have achieved great success in many fields, and recent
works have studied exploring LLMs for graph discriminative tasks such as node …