8 papers
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding
Zhongjian Zhang, Yue Yu, Mengmei Zhang +3
The remarkable success of large language models (LLMs) has motivated researchers to adapt them as universal predictors for various graph tasks. As a widely recognized paradigm, Gra…
Toward Graph-Tokenizing Large Language Models with Reconstructive Graph Instruction Tuning
Zhongjian Zhang, Xiao Wang, Mengmei Zhang +2
The remarkable success of large language models (LLMs) has motivated researchers to adapt them as universal predictors for various graph-related tasks, with the ultimate goal of de…
Advancing Molecular Graph-Text Pre-training via Fine-grained Alignment
Yibo Li, Yuan Fang, Mengmei Zhang +1
Understanding molecular structure and related knowledge is crucial for scientific research. Recent studies integrate molecular graphs with their textual descriptions to enhance mol…
Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models
Zhibiao Wang, Yunlong Zhou, Ziwei Zhang +4
Graph Transformers, leveraging the global attention to capture long-range dependencies in graph structures, have significantly advanced graph machine learning, but face prohibitive…
Data-centric Federated Graph Learning with Large Language Models
Bo Yan, Zhongjian Zhang, Huabin Sun +3
In federated graph learning (FGL), a complete graph is divided into multiple subgraphs stored in each client due to privacy concerns, and all clients jointly train a global graph m…
Graph Foundation Models: Concepts, Opportunities and Challenges
Jiawei Liu, Cheng Yang, Zhiyuan Lu +8
Foundation models have emerged as critical components in a variety of artificial intelligence applications, and showcase significant success in natural language processing and seve…