skip to main content
10.1145/3633637.3633687acmotherconferencesArticle/Chapter ViewAbstractPublication PagesiccprConference Proceedingsconference-collections
research-article

A Semi-Supervised Graph Neural Network with Confidence Discrimination

Published: 28 February 2024 Publication History

Abstract

Existing graph neural network methods usually depend on a large amount of labeled data, but labeled data is often scarce in the real world. In the case of less labeled data, utilizing correct pseudo-labels for model training can improve the model performance effectively. However, existing pseudo-labeling methods often use a fixed confidence threshold for all classes, leading to class imbalance and low data utilization. To solve this problem, a semi-supervised graph neural network method based on confidence discrimination is proposed, which can make full use of unlabeled data to facilitate semi-supervised node classification. Our method considers the learning state and difficulty of different classes of nodes and designs an adaptive confidence discrimination module. It assigns different confidence thresholds to each class of node, and uses unlabeled nodes with high confidence to expand the label set continuously. Our method can learn more discriminative node features to improve the model performance. On five publicly available datasets, the accuracy of the proposed method is improved by 2.1% on average compared with other methods, and in particular by 5.7% on the Flickr dataset. Extensive experiments verify the effectiveness of the proposed method.

References

[1]
S. Wu, F. Sun, W. Zhang, X. Xie, and B. Cui, “Graph neural networks in recommender systems: a survey,” ACM Computing Surveys, vol. 55, no. 5, pp. 1–37, 2022.
[2]
R. Ramirez, Y.-C. Chiu, A. Hererra, M. Mostavi, J. Ramirez, Y. Chen, Y. Huang, and Y.-F. Jin, “Classification of cancer types using graph convolutional neural networks,” Frontiers in Physics, vol. 8, p. 203, 2020.
[3]
Zhang, Yiding and Wang, Xiao and Shi, Chuan and Jiang, Xunqiang and Ye, Yanfang, “Hyperbolic graph attention network,” IEEE Transactions on Big Data, vol. 8, no. 6, pp. 1690–1701, 2021.
[4]
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in Proceedings of the 5th International Conference on Learning Representations (ICLR), 2017.
[5]
Y. Liu, X. Wang, S. Wu, and Z. Xiao, “Independence promoted graph disentangled networks,” in Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI), vol. 34, no. 04, 2020, pp. 4916–4923.
[6]
D.-H. Lee, “Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks,” in ICML Workshop on Challenges in Representation Learning, vol. 3, no. 2, 2013, p. 896.
[7]
M. Gong, H. Zhou, A. Qin, W. Liu, and Z. Zhao, “Self-paced co-training of graph neural networks for semi-supervised node classification,” IEEE Transactions on Neural Networks and Learning Systems, 2022.
[8]
E. Arazo, D. Ortego, P. Albert, N. E. O'Connor, and K. McGuinness, “Pseudo-labeling and confirmation bias in deep semi-supervised learning,” in 2020 International Joint Conference on Neural Networks (IJCNN), 2020, pp. 1–t.
[9]
X. Zhu, Z. Ghahramani, and J. D. Lafferty, “Semi-supervised learning using gaussian fields and harmonic functions,” in Proceedings of the 20th International Conference on Machine Learning (ICML), 2003, pp. 912–919.
[10]
D. Zhou, O. Bousquet, T. Lal, J. Weston, and B. Scholkopf, “Learning ¨ with local and global consistency,” in Advances in Neural Information Processing Systems, vol. 16, 2003.
[11]
J. Weston, F. Ratle, H. Mobahi, and R. Collobert, “Deep learning via semi-supervised embedding,” in Neural Networks: Tricks of the Trade. Springer, 2012, pp. 639–655.
[12]
B. Perozzi, R. Al-Rfou, and S. Skiena, “Deepwalk: Online learning of social representations,” in Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2014, pp. 701–710.
[13]
J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei, “Line: Largescale information network embedding,” in Proceedings of International Conference on World Wide Web, 2015, pp. 1067–1077.
[14]
A. Grover and J. Leskovec, “node2vec: Scalable feature learning for networks,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 855– 864.
[15]
Y. Liu, S. Zhao, X. Wang, L. Geng, Z. Xiao, and J. C.-W. Lin, “Selfconsistent graph neural networks for semi-supervised node classification,” IEEE Transactions on Big Data, 2023.
[16]
A. Iscen, G. Tolias, Y. Avrithis, and O. Chum, “Label propagation for deep semi-supervised learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 5070–5079.
[17]
W. Shi, Y. Gong, C. Ding, Z. M. Tao, and N. Zheng, “Transductive semi-supervised deep learning using min-max features,” in Proceedings of the 15th European Conference on Computer Vision (ECCV), 2018, pp. 299–315.
[18]
Y. Xu, L. Shang, J. Ye, Q. Qian, Y.-F. Li, B. Sun, H. Li, and R. Jin, “Dash: Semi-supervised learning with dynamic thresholding,” in Proceedings of the 38th International Conference on Machine Learning (ICML), 2021, pp. 11 525–11 536.
[19]
W. Lu, Z. Guan, W. Zhao, Y. Yang, Y. Lv, B. Yu, and D. Tao, “Pseudo contrastive learning for graph-based semi-supervised learning,” arXiv preprint arXiv:2302.09532, 2023.
[20]
Z. Zhou, S. Zhang, and Z. Huang, “Dynamic self-training framework for graph convolutional networks,” arXiv preprint arXiv:1910.02684, 2019.
[21]
X. Wang, H. Liu, C. Shi, and C. Yang, “Be confident! towards trustworthy graph neural networks via confidence calibration,” Advances in Neural Information Processing Systems, vol. 34, pp. 23 768–23 779, 2021.
[22]
K. Hassani and A. H. Khasahmadi, “Contrastive multi-view representation learning on graphs,” in Proceedings of the 37th International Conference on Machine Learning (ICML), 2020, pp. 4116–4126.
[23]
L. Page, S. Brin, R. Motwani, and T. Winograd, “The pagerank citation ranking: Bringing order to the web.” Stanford InfoLab, Tech. Rep., 1999.
[24]
Z. Yang, W. Cohen, and R. Salakhudinov, “Revisiting semi-supervised learning with graph embeddings,” in Proceedings of the 33rd International Conference on Machine Learning (ICML), 2016, pp. 40–48.
[25]
X. Wang, H. Ji, C. Shi, B. Wang, Y. Ye, P. Cui, and P. S. Yu, “Heterogeneous graph attention network,” in The World Wide Web Conference, 2019, pp. 2022–2032.
[26]
Z. Meng, S. Liang, H. Bao, and X. Zhang, “Co-embedding attributed networks,” in Proceedings of the 12th ACM International Conference on Web Search and Data Mining, 2019, pp. 393–401.
[27]
W. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” Advances in Neural Information Processing Systems, pp. 1024–1034, 2017.
[28]
P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio, “Graph attention networks,” in Proceedings of the 6th International Conference on Learning Representations (ICLR), 2018.
[29]
S. Abu-El-Haija, B. Perozzi, A. Kapoor, N. Alipourfard, K. Lerman, H. Harutyunyan, G. Ver Steeg, and A. Galstyan, “Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing,” in Proceedings of the 36th International Conference on Machine Learning (ICML), 2019, pp. 21–29.
[30]
Guo, Kai and Zhou, Kaixiong and Hu, Xia and Li, Yu and Chang, Yi and Wang, Xin, “Orthogonal graph neural networks,” in Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI), vol. 36, no. 4, 2022, pp. 3996–4004.
[31]
J. Lee, Y. Oh, Y. In, N. Lee, D. Hyun, and C. Park, “Grafn: Semisupervised node classification on graph with few labels via nonparametric distribution assignment,” in Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022, pp. 2243–2248.
[32]
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in Proceedings of the 6th International Conference on Learning Representations (ICLR), 2015.
[33]
L. Van Der Maaten, “Accelerating t-sne using tree-based algorithms,” The Journal of Machine Learning Research, vol. 15, no. 1, pp. 3221– 3245, 2014.

Index Terms

  1. A Semi-Supervised Graph Neural Network with Confidence Discrimination

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Other conferences
      ICCPR '23: Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition
      October 2023
      589 pages
      ISBN:9798400707988
      DOI:10.1145/3633637
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 28 February 2024

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. Confidence discrimination
      2. Graph neural network
      3. Pseudo-label learning

      Qualifiers

      • Research-article
      • Research
      • Refereed limited

      Funding Sources

      Conference

      ICCPR 2023

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • 0
        Total Citations
      • 39
        Total Downloads
      • Downloads (Last 12 months)39
      • Downloads (Last 6 weeks)3
      Reflects downloads up to 20 Feb 2025

      Other Metrics

      Citations

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      HTML Format

      View this article in HTML Format.

      HTML Format

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media