GAugLLM: Improving Graph Contrastive Learning for Text-Attributed Graphs with Large Language Models

Author:

Fang Yi1ORCID,Fan Dongzhe1ORCID,Zha Daochen2ORCID,Tan Qiaoyu1ORCID

Affiliation:

1. SFSC of AI and DL, New York University (Shanghai), Shanghai, China

2. Department of Computer Science, Rice University, Huston, USA

Funder

New York University Shanghai

Shanghai Frontiers Science Center of Artificial Intelligence and Deep Learning

Publisher

ACM

Reference54 articles.

1. Graph Barlow Twins: A self-supervised representation learning framework for graphs

2. 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. arXiv preprint arXiv:2307.03393 (2023).

3. Eli Chien, Wei-Cheng Chang, Cho-Jui Hsieh, Hsiang-Fu Yu, Jiong Zhang, Olgica Milenkovic, and Inderjit S Dhillon. 2022. Node feature extraction by self-supervised multi-scale neighborhood prediction. In International Conference on Learning Representations.

4. Data Augmentation for Deep Graph Learning

5. Matthias Fey and Jan Eric Lenssen. 2019. Fast graph representation learning with PyTorch Geometric. arXiv preprint arXiv:1903.02428 (2019).

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