HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks-Reference-Cited by-同舟云学术

HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks

Published:2024-05-13 Issue: Volume: Page:1015-1023
ISSN:
Container-title:Proceedings of the ACM Web Conference 2024
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Author:

Ma Yihong¹^ORCID,Yan Ning²^ORCID,Li Jiayu³^ORCID,Mortazavi Masood²^ORCID,Chawla Nitesh V.¹^ORCID

Affiliation:

1. University of Notre Dame, Notre Dame, Indiana, USA

2. Futurewei Technologies Inc., Santa Clara, California, USA

3. Syracuse University, Syracuse, New York, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3589334.3645685

Reference44 articles.

1. Hyojin Bahng, Ali Jahanian, Swami Sankaranarayanan, and Phillip Isola. 2022. Exploring visual prompts for adapting large-scale models. arXiv preprint arXiv:2203.17274 (2022).

2. Andrew Brock Theodore Lim James M Ritchie and Nick Weston. 2017. Neural photo editing with introspective adversarial networks. In ICLR.

3. Yuwei Cao Hao Peng Jia Wu Yingtong Dou Jianxin Li and Philip S Yu. 2021. Knowledge-preserving incremental social event detection via heterogeneous gnns. In WWW.

4. Wenqi Fan Yao Ma Qing Li Yuan He Eric Zhao Jiliang Tang and Dawei Yin. 2019. Graph neural networks for social recommendation. In WWW.

5. Taoran Fang Yunchao Zhang Yang Yang Chunping Wang and Lei Chen. 2023. Universal prompt tuning for graph neural networks. In NeurIPS.

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