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Related Experiment Video

Updated: Nov 27, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

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Active Learning for Node Classification: An Evaluation.

Kaushalya Madhawa1, Tsuyoshi Murata1

  • 1Department of Computer Science, Tokyo Institute of Technology, Tokyo 152-8552, Japan.

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary
This summary is machine-generated.

Active learning trains models with less data by selecting informative instances. For graph neural networks, existing methods fail; a new exploration term improves performance on graph-structured data.

Keywords:
active learninggraph neural networksgraph representation learningmachine learningnode classification

Related Experiment Videos

Last Updated: Nov 27, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.8K

Area of Science:

  • Machine Learning
  • Graph Neural Networks
  • Data Science

Background:

  • Deep neural networks require substantial labeled data for training.
  • Active learning addresses data scarcity by selecting informative instances for labeling.
  • Graph neural networks (GNNs) excel at attributed graph classification but need hyperparameter tuning.

Purpose of the Study:

  • To investigate the effectiveness of active learning strategies for attributed graph classification.
  • To compare existing active learning algorithms on graph data against their performance on other data types.
  • To propose an improved active learning approach for GNNs in label-scarce settings.

Main Methods:

  • A comparative analysis of state-of-the-art active learning algorithms on attributed graphs.
  • Evaluation of algorithms designed for image and text data when applied to graph structures.
  • Development and testing of a novel count-based exploration term within an active learning framework for GNNs.

Main Results:

  • Active learning algorithms optimized for images and text demonstrate suboptimal performance on attributed graphs.
  • The proposed active learning method, incorporating a count-based exploration term, shows improved efficacy.
  • Empirical results on benchmark graphs confirm the necessity of exploration-based strategies for GNN active learning.

Conclusions:

  • Standard active learning techniques are not directly transferable to graph neural network applications.
  • Integrating exploration-vs.-exploitation considerations is crucial for effective active learning on graph data.
  • The novel exploration term enhances uncertainty-based active learning for GNNs, particularly in low-data regimes.