Supervised Graph Contrastive Learning for Few-Shot Node Classification

Zhen Tan; Kaize Ding; Ruocheng Guo; Huan Liu

doi:10.1007/978-3-031-26390-3_24

Supervised Graph Contrastive Learning for Few-Shot Node Classification

Zhen Tan^*, Kaize Ding, Ruocheng Guo, Huan Liu

^*Corresponding author for this work

Statistics and Data Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 Scopus citations

Abstract

Graphs present in many real-world applications, such as financial fraud detection, commercial recommendation, and social network analysis. But given the high cost of graph annotation or labeling, we face a severe graph label-scarcity problem, i.e., a graph might have a few labeled nodes. One example of such a problem is the so-called few-shot node classification. A predominant approach to this problem resorts to episodic meta-learning. In this work, we challenge the status quo by asking a fundamental question whether meta-learning is a must for few-shot node classification tasks. We propose a new and simple framework under the standard few-shot node classification setting as an alternative to meta-learning to learn an effective graph encoder. The framework consists of supervised graph contrastive learning with novel mechanisms for data augmentation, subgraph encoding, and multi-scale contrast on graphs. Extensive experiments on three benchmark datasets (CoraFull, Reddit, Ogbn) show that the new framework significantly outperforms state-of-the-art meta-learning based methods.

Original language	English (US)
Title of host publication	Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Proceedings
Editors	Massih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	394-411
Number of pages	18
ISBN (Print)	9783031263897
DOIs	https://doi.org/10.1007/978-3-031-26390-3_24
State	Published - 2023
Event	22nd Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2022 - Grenoble, France Duration: Sep 19 2022 → Sep 23 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13714 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	22nd Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2022
Country/Territory	France
City	Grenoble
Period	9/19/22 → 9/23/22

Funding

Acknowledgments. This work is partially supported by Army Research Office (ARO) W911NF2110030 and Army Research Lab (ARL) W911NF2020124.

Keywords

Few-shot learning
Graph Neural Networks
Graph contrastive learning

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-031-26390-3_24

Cite this

Tan, Z., Ding, K., Guo, R., & Liu, H. (2023). Supervised Graph Contrastive Learning for Few-Shot Node Classification. In M.-R. Amini, S. Canu, A. Fischer, T. Guns, P. Kralj Novak, & G. Tsoumakas (Eds.), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Proceedings (pp. 394-411). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13714 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-26390-3_24

Tan, Zhen ; Ding, Kaize ; Guo, Ruocheng et al. / Supervised Graph Contrastive Learning for Few-Shot Node Classification. Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Proceedings. editor / Massih-Reza Amini ; Stéphane Canu ; Asja Fischer ; Tias Guns ; Petra Kralj Novak ; Grigorios Tsoumakas. Springer Science and Business Media Deutschland GmbH, 2023. pp. 394-411 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Tan, Z, Ding, K, Guo, R & Liu, H 2023, Supervised Graph Contrastive Learning for Few-Shot Node Classification. in M-R Amini, S Canu, A Fischer, T Guns, P Kralj Novak & G Tsoumakas (eds), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13714 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 394-411, 22nd Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2022, Grenoble, France, 9/19/22. https://doi.org/10.1007/978-3-031-26390-3_24

Supervised Graph Contrastive Learning for Few-Shot Node Classification. / Tan, Zhen; Ding, Kaize; Guo, Ruocheng et al.
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Proceedings. ed. / Massih-Reza Amini; Stéphane Canu; Asja Fischer; Tias Guns; Petra Kralj Novak; Grigorios Tsoumakas. Springer Science and Business Media Deutschland GmbH, 2023. p. 394-411 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13714 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - Graphs present in many real-world applications, such as financial fraud detection, commercial recommendation, and social network analysis. But given the high cost of graph annotation or labeling, we face a severe graph label-scarcity problem, i.e., a graph might have a few labeled nodes. One example of such a problem is the so-called few-shot node classification. A predominant approach to this problem resorts to episodic meta-learning. In this work, we challenge the status quo by asking a fundamental question whether meta-learning is a must for few-shot node classification tasks. We propose a new and simple framework under the standard few-shot node classification setting as an alternative to meta-learning to learn an effective graph encoder. The framework consists of supervised graph contrastive learning with novel mechanisms for data augmentation, subgraph encoding, and multi-scale contrast on graphs. Extensive experiments on three benchmark datasets (CoraFull, Reddit, Ogbn) show that the new framework significantly outperforms state-of-the-art meta-learning based methods.

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Tan Z, Ding K, Guo R, Liu H. Supervised Graph Contrastive Learning for Few-Shot Node Classification. In Amini MR, Canu S, Fischer A, Guns T, Kralj Novak P, Tsoumakas G, editors, Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 394-411. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-26390-3_24