Pseudo-Labeling with Graph Active Learning for Few-shot Node Classification

Quan Li, Lingwei Chen, Shixiong Jing, Dinghao Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Graphs have emerged as one of the most important and powerful data structures to perform content analysis in many fields. In this line of work, node classification is a classic task, which is generally performed using graph neural networks (GNNs). Unfortunately, regular GNNs cannot be well generalized into the real-world application scenario when the labeled nodes are few. To address this challenge, we propose a novel few-shot node classification model that leverages pseudo-labeling with graph active learning. We first provide a theoretical analysis to argue that extra unlabeled data benefit few-shot classification. Inspired by this, our model proceeds by performing multi-level data augmentation with consistency and contrastive regularizations for better semi-supervised pseudo-labeling, and further devising graph active learning to facilitate pseudo-label selection and improve model effectiveness. Extensive experiments on four public citation networks have demonstrated that our model can effectively improve node classification accuracy with considerably few labeled data, which significantly outperforms all state-of-the-art baselines by large margins.

Original languageEnglish (US)
Title of host publicationProceedings - 23rd IEEE International Conference on Data Mining, ICDM 2023
EditorsGuihai Chen, Latifur Khan, Xiaofeng Gao, Meikang Qiu, Witold Pedrycz, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9798350307887
StatePublished - 2023
Event23rd IEEE International Conference on Data Mining, ICDM 2023 - Shanghai, China
Duration: Dec 1 2023Dec 4 2023

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Conference23rd IEEE International Conference on Data Mining, ICDM 2023

All Science Journal Classification (ASJC) codes

  • General Engineering

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