Can GNN be Good Adapter for LLMs?

Author:

Huang Xuanwen1ORCID,Han Kaiqiao1ORCID,Yang Yang1ORCID,Bao Dezheng1ORCID,Tao Quanjin1ORCID,Chai Ziwei1ORCID,Zhu Qi2ORCID

Affiliation:

1. Zhejiang University, Hangzhou, China

2. Amazon Web Services, Santa Clara, USA

Funder

National Natural Science Foundation of China

Publisher

ACM

Reference43 articles.

1. Claude Berge. 2001. The theory of graphs. In Courier Corporation.

2. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. In NeurIPS'20, Vol. 33. 1877--1901.

3. Zhikai Chen Haitao Mao Hang Li Wei Jin Hongzhi Wen Xiaochi Wei Shuaiqiang Wang Dawei Yin Wenqi Fan Hui Liu et al. 2023. Exploring the potential of large language models (llms) in learning on graphs. In arXiv preprint arXiv:2307.03393.

4. Eli Chien Wei-Cheng Chang Cho-Jui Hsieh Hsiang-Fu Yu Jiong Zhang Olgica Milenkovic and Inderjit S Dhillon. 2021. Node feature extraction by self-supervised multi-scale neighborhood prediction. In arXiv preprint arXiv:2111.00064.

5. Courtney D Corley, Diane J Cook, Armin R Mikler, and Karan P Singh. 2010. Text and structural data mining of influenza mentions in web and social media. In International journal of environmental research and public health, Vol. 7. 596--615.

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