Cargando…
Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node Classification
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 imb...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8655128/ https://www.ncbi.nlm.nih.gov/pubmed/34899230 http://dx.doi.org/10.3389/fnbot.2021.775688 |
_version_ | 1784612017182605312 |
---|---|
author | Shi, Shuhao Qiao, Kai Yang, Shuai Wang, Linyuan Chen, Jian Yan, Bin |
author_facet | Shi, Shuhao Qiao, Kai Yang, Shuai Wang, Linyuan Chen, Jian Yan, Bin |
author_sort | Shi, Shuhao |
collection | PubMed |
description | 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 Traditional methods such as resampling, reweighting, and synthetic samples that deal with imbalanced datasets are no longer applicable in GNN.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 study 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 classifiers, 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 graph convolutional network (GCN), GraphSAGE, graph attention network (GAT), simplifying graph convolutional networks (SGC), multi-scale graph convolution networks (N-GCN), and most advanced reweighting and resampling methods on synthetic imbalanced datasets, with an average performance improvement of 4.5%. |
format | Online Article Text |
id | pubmed-8655128 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-86551282021-12-10 Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node Classification Shi, Shuhao Qiao, Kai Yang, Shuai Wang, Linyuan Chen, Jian Yan, Bin Front Neurorobot Neuroscience 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 study 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 classifiers, 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 graph convolutional network (GCN), GraphSAGE, graph attention network (GAT), simplifying graph convolutional networks (SGC), multi-scale graph convolution networks (N-GCN), and most advanced reweighting and resampling methods on synthetic imbalanced datasets, with an average performance improvement of 4.5%. Frontiers Media S.A. 2021-11-25 /pmc/articles/PMC8655128/ /pubmed/34899230 http://dx.doi.org/10.3389/fnbot.2021.775688 Text en Copyright © 2021 Shi, Qiao, Yang, Wang, Chen and Yan. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Shi, Shuhao Qiao, Kai Yang, Shuai Wang, Linyuan Chen, Jian Yan, Bin Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node Classification |
title | Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node Classification |
title_full | Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node Classification |
title_fullStr | Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node Classification |
title_full_unstemmed | Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node Classification |
title_short | Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node Classification |
title_sort | boosting-gnn: boosting algorithm for graph networks on imbalanced node classification |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8655128/ https://www.ncbi.nlm.nih.gov/pubmed/34899230 http://dx.doi.org/10.3389/fnbot.2021.775688 |
work_keys_str_mv | AT shishuhao boostinggnnboostingalgorithmforgraphnetworksonimbalancednodeclassification AT qiaokai boostinggnnboostingalgorithmforgraphnetworksonimbalancednodeclassification AT yangshuai boostinggnnboostingalgorithmforgraphnetworksonimbalancednodeclassification AT wanglinyuan boostinggnnboostingalgorithmforgraphnetworksonimbalancednodeclassification AT chenjian boostinggnnboostingalgorithmforgraphnetworksonimbalancednodeclassification AT yanbin boostinggnnboostingalgorithmforgraphnetworksonimbalancednodeclassification |