DFGNN: Dual-frequency Graph Neural Network for Sign-aware Feedback-Reference-Cited by-同舟云学术

DFGNN: Dual-frequency Graph Neural Network for Sign-aware Feedback

Published:2024-08-24 Issue: Volume: Page:3437-3447
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
language:
Short-container-title:

Author:

Wu Yiqing¹^ORCID,Xie Ruobing²^ORCID,Zhang Zhao³^ORCID,Zhang Xu²^ORCID,Zhuang Fuzhen⁴^ORCID,Lin Leyu²^ORCID,Kang Zhanhui²^ORCID,Xu Yongjun³^ORCID

Affiliation:

1. Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China

2. Tencent, Beijing, China

3. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

4. Institute of Artificial Intelligence, Beihang University & Zhongguancun Laboratory, Beijing, China

Funder

Young Elite Scientists Sponsorship Program by CAST

the National Natural Science Foundation of China under Grant

the National Key Research and Development Program of China under Grant

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671701

Reference46 articles.

1. Jinbin Bai, Chunhui Liu, Feiyue Ni, Haofan Wang, Mengying Hu, Xiaofeng Guo, and Lele Cheng. 2022. Lat: Latent translation with cycle-consistency for video-text retrieval. arXiv preprint arXiv:2207.04858 (2022).

2. Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 2013. Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203 (2013).

3. Chen Cai and Yusu Wang. 2020. A note on over-smoothing for graph neural networks. arXiv preprint arXiv:2006.13318 (2020).

4. Deep Neural Networks for YouTube Recommendations

5. Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. Advances in neural information processing systems, Vol. 29 (2016).