A Survey of Large Language Models for Graphs

Author:

Ren Xubin1ORCID,Tang Jiabin1ORCID,Yin Dawei2ORCID,Chawla Nitesh3ORCID,Huang Chao1ORCID

Affiliation:

1. University of Hong Kong, Hong Kong, China

2. Baidu, Beijing, China

3. University of Notre Dame, Indiana, USA

Publisher

ACM

Reference100 articles.

1. Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023).

2. Qihang Ai, Jianwu Zhou, Haiyun Jiang, Lemao Liu, and Shuming Shi. 2023. When Graph Data Meets Multimodal: A New Paradigm for Graph Understanding and Reasoning. arXiv preprint arXiv:2312.10372 (2023).

3. William Brannon et al. 2023. Congrat: Self-supervised contrastive pretraining for joint graph and text embeddings. arXiv preprint arXiv:2305.14321 (2023).

4. He Cao, Zijing Liu, Xingyu Lu, Yuan Yao, and Yu Li. 2023. Instructmol: Multi-modal integration for building a versatile and reliable molecular assistant in drug discovery. arXiv preprint arXiv:2311.16208 (2023).

5. Ziwei Chai, Tianjie Zhang, Liang Wu, Kaiqiao Han, Xiaohai Hu, Xuanwen Huang, and Yang Yang. 2023. Graphllm: Boosting graph reasoning ability of large language model. arXiv preprint arXiv:2310.05845 (2023).

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