Abstract

For real-world graph data, the node class distribution is inherently imbalanced and long-tailed, which naturally leads to a few-shot learning scenario with limited nodes labeled for newly emerging classes. Existing efforts are carefully designed to solve such a few-shot learning problem via data augmentation, learning transferable initialization, to name a few. However, most, if not all, of them are based on a strong assumption that all the test nodes must exclusively come from novel classes, which is impractical in real-world applications. In this paper, we study a broader and more realistic problem named generalized few-shot node classification, where the test samples can be from both novel classes and base classes. Compared with the standard fewshot node classification, this new problem imposes several unique challenges, including asymmetric classification and inconsistent preference. To counter those challenges, we propose a shot-aware graph neural network (STAGER) equipped with an uncertainty-based weight assigner module for adaptive propagation. To formulate this problem from the meta-learning perspective, we propose a new training paradigm named imbalanced episodic training to ensure the label distribution is consistent between the training and test scenarios. Experiment results on four real-world datasets demonstrate the efficacy of our model, with up to 14% accuracy improvement over baselines.

Original languageEnglish (US)
Title of host publicationProceedings - 22nd IEEE International Conference on Data Mining, ICDM 2022
EditorsXingquan Zhu, Sanjay Ranka, My T. Thai, Takashi Washio, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages608-617
Number of pages10
ISBN (Electronic)9781665450997
DOIs
StatePublished - 2022
Event22nd IEEE International Conference on Data Mining, ICDM 2022 - Orlando, United States
Duration: Nov 28 2022Dec 1 2022

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
Volume2022-November

Conference

Conference22nd IEEE International Conference on Data Mining, ICDM 2022
Country/TerritoryUnited States
CityOrlando
Period11/28/2212/1/22

Keywords

  • graph mining
  • meta-learning
  • node classification

ASJC Scopus subject areas

  • General Engineering

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