Noise Perturbation Based Graph Contrastive Learning via Flexible Filters for Node Classification

被引：0
|
作者：
Xiong, Zhilong [1 ]
Cai, Jia [2 ]
Yan, Ranhui [3 ]
Huang, Xiaolin [4 ]
机构：
[1] XFusion Digital Technologies Company Limited, Shenzhen, China
[2] Guangdong University of Finance & Economics, School of Digital Economics, Guangzhou, China
[3] Guangdong University of Finance & Economics, School of Statistics and Mathematics, Guangzhou, China
[4] Shanghai Jiao Tong University, Institute of Image Processing and Pattern Recognition, Shanghai, China
来源：
Proceedings of the International Joint Conference on Neural Networks | 2024年
关键词：
Compilation and indexing terms; Copyright 2025 Elsevier Inc;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Convolutional neural networks - Graph neural networks - High pass filters - Low pass filters
引用
收藏
相关论文
共 50 条
  • [1] Line graph contrastive learning for node classification
    Li, Mingyuan
    Meng, Lei
    Ye, Zhonglin
    Xiao, Yuzhi
    Cao, Shujuan
    Zhao, Haixing
    JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES, 2024, 36 (04)
  • [2] Perturbation-augmented Graph Convolutional Networks: A Graph Contrastive Learning architecture for effective node classification tasks
    Guo, Qihang
    Yang, Xibei
    Zhang, Fengjun
    Xu, Taihua
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2024, 129
  • [3] ImGCL: Revisiting Graph Contrastive Learning on Imbalanced Node Classification
    Zeng, Liang
    Li, Lanqing
    Gao, Ziqi
    Zhao, Peilin
    Li, Jian
    THIRTY-SEVENTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 37 NO 9, 2023, : 11138 - 11146
  • [4] GCL: Contrastive learning instead of graph convolution for node classification
    Li, Shu
    Han, Lixin
    Wang, Yang
    Pu, Yonglin
    Zhu, Jun
    Li, Jingxian
    NEUROCOMPUTING, 2023, 551
  • [5] Co-Modality Graph Contrastive Learning for Imbalanced Node Classification
    Qian, Yiyue
    Zhang, Chunhui
    Zhang, Yiming
    Wen, Qianlong
    Ye, Yanfang
    Zhang, Chuxu
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 35 (NEURIPS 2022), 2022,
  • [6] Supervised Graph Contrastive Learning for Few-Shot Node Classification
    Tan, Zhen
    Ding, Kaize
    Guo, Ruocheng
    Liu, Huan
    MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2022, PT II, 2023, 13714 : 394 - 411
  • [7] A Unique Framework of Heterogeneous Augmentation Graph Contrastive Learning for Both Node and Graph Classification
    Shao, Qi
    Chen, Duxin
    Yu, Wenwu
    IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING, 2024, 11 (06): : 5818 - 5828
  • [8] Graph Contrastive Learning Method with Sample Disparity Constraint and Feature Structure Graph for Node Classification
    Chen, Gangbin
    Cheng, Junwei
    Liang, Wanying
    He, Chaobo
    Tang, Yong
    KNOWLEDGE SCIENCE, ENGINEERING AND MANAGEMENT, PT IV, KSEM 2023, 2023, 14120 : 291 - 303
  • [9] Robust graph contrastive learning with multi-hop views for node classification
    Wang, Yutong
    Zhang, Junheng
    Cao, Ren
    Zou, Minhao
    Guan, Chun
    Leng, Siyang
    APPLIED SOFT COMPUTING, 2025, 171
  • [10] Universal Graph Contrastive Learning with a Novel Laplacian Perturbation
    Ko, Taewook
    Choi, Yoonhyuk
    Kim, Chong-Kwon
    UNCERTAINTY IN ARTIFICIAL INTELLIGENCE, 2023, 216 : 1098 - 1108
12345