Zeyuan Guo

2 accepted papers

2026

Graph Tokenization for Bridging Graphs and Transformers

Zeyuan Guo, Enmao Diao, Cheng Yang, Chuan Shi

ICLR 2026poster

The success of large pretrained Transformers is closely tied to tokenizers, which convert raw input into discrete symbols. Extending these models to graph-structured data remains a significant challenge. In this work, we introduce a graph tokenization framework that generates sequential representat…

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2026

Graph4LLM: A Systematic Survey of Graph-Enhanced Large Language Models

Xinyan Zhu, Cheng Yang, Qiuyu Wang, Zeyuan Guo, Yiding Wang, Zedi Liu +2

IJCAI 2026

Large Language Models (LLMs) excel in natural language processing (NLP) tasks. However, they suffer from inherent limitations due to their sequence-based nature, such as structural information loss and factual unreliability. Graphs, with the ability to explicitly model entities and relations, offer

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