mlsanityData to 14 Oct 2026

Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node Classification

arXiv 2105.11625 ↗First posted 25 May 2021cs.LG cs.CVcited by 10

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Abstract (arXiv)

The Graph Neural Network (GNN) has been widely used for graph data representation. However, the existing researches only consider the ideal balanced dataset, and the imbalanced dataset is rarely considered. Traditional methods such as resampling, reweighting, and synthetic samples that deal with imbalanced datasets are no longer applicable in GNN. This paper proposes an ensemble model called Boosting-GNN, which uses GNNs as the base classifiers during boosting. In Boosting-GNN, higher weights are set for the training samples that are not correctly classified by the previous classifier, thus achieving higher classification accuracy and better reliability. Besides, transfer learning is used to reduce computational cost and increase fitting ability. Experimental results indicate that the proposed Boosting-GNN model achieves better performance than GCN, GraphSAGE, GAT, SGC, N-GCN, and most advanced reweighting and resampling methods on synthetic imbalanced datasets, with an average performance improvement of 4.5%

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 25 of its 54 references that are in the map. First posted on arXiv 25 May 2021.

Builds on

25 of 54 references.

References · 25

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13 more
  1. 2020Adaptive Graph Convolutional Recurrent Network for Traffic Forecastingcited by 530
  2. 2019DeepGCNs: Can GCNs Go as Deep as CNNs?cited by 527
  3. 2017A systematic study of the class imbalance problem in convolutional neural networkscited by 513
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  7. 2020Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approachcited by 120
  8. 2018N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classificationcited by 54
  9. 2021Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networkscited by 38
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Cited by year

Citing papers per year: 2022: 3; 2023: 3; 2024: 2; 2025: 2.

10 papers.

  1. 2024Graph Neural Networks for Financial Fraud Detection: A Reviewcited by 25
  2. 2023Over-Sampling Strategy in Feature Space for Graphs based Class-imbalanced Bot Detectioncited by 7
  3. 2023RF-GNN: Random Forest Boosted Graph Neural Network for Social Bot Detectioncited by 5
  4. 2022A Class-Aware Representation Refinement Framework for Graph Classificationcited by 3
  5. 2023Muti-scale Graph Neural Network with Signed-attention for Social Bot Detection: A Frequency Perspectivecited by 2
  6. 2025RCCFormer: A Robust Crowd Counting Network Based on Transformercited by 1
  7. 2022Imbalanced Node Processing Method in Graph Neural Network Classification Taskcited by 0
  8. 2022ReGrAt: Regularization in Graphs using Attention to handle class imbalancecited by 0
  9. 2024RW-NSGCN: A Robust Approach to Structural Attacks via Negative Samplingcited by 0
  10. 2025Graph Neural Network-based Spectral Filtering Mechanism for Imbalance Classification in Network Digital Twincited by 0