CSC-GCN: Contrastive semantic calibration for graph convolution network
Graph convolutional networks (GCNs) have been successfully applied to node representation learning in various real-world applications. However, the performance of GCNs drops rapidly when the labeled data are severely scarce, and the node features are prone to being indistinguishable with stacking mo...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2023-11-01
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| Series: | Journal of Information and Intelligence |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949715923000598 |
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| author | Xu Yang Kun Wei Cheng Deng |
| author_facet | Xu Yang Kun Wei Cheng Deng |
| author_sort | Xu Yang |
| collection | DOAJ |
| description | Graph convolutional networks (GCNs) have been successfully applied to node representation learning in various real-world applications. However, the performance of GCNs drops rapidly when the labeled data are severely scarce, and the node features are prone to being indistinguishable with stacking more layers, causing over-fitting and over-smoothing problems. In this paper, we propose a simple yet effective contrastive semantic calibration for graph convolution network (CSC-GCN), which integrates stochastic identity aggregation and semantic calibration to overcome these weaknesses. The basic idea is the node features obtained from different aggregation operations should be similar. Toward that end, identity aggregation is utilized to extract semantic features from labeled nodes, while stochastic label noise is adopted to alleviate the over-fitting problem. Then, contrastive learning is employed to improve the discriminative ability of the node features, and the features from different aggregation operations are calibrated according to the class center similarity. In this way, the similarity between unlabeled features and labeled ones from the same class is enhanced while effectively reducing the over-smoothing problem. Experimental results on eight popular datasets show that the proposed CSC-GCN outperforms state-of-the-art methods on various classification tasks. |
| format | Article |
| id | doaj-art-042ccebc4ae44243944d0ea4681692b3 |
| institution | Kabale University |
| issn | 2949-7159 |
| language | English |
| publishDate | 2023-11-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Journal of Information and Intelligence |
| spelling | doaj-art-042ccebc4ae44243944d0ea4681692b32025-08-20T03:32:54ZengKeAi Communications Co., Ltd.Journal of Information and Intelligence2949-71592023-11-011429530710.1016/j.jiixd.2023.10.001CSC-GCN: Contrastive semantic calibration for graph convolution networkXu Yang0Kun Wei1Cheng Deng2School of Electronic Engineering, Xidian University, Xi'an 710071, ChinaSchool of Electronic Engineering, Xidian University, Xi'an 710071, ChinaCorresponding author.; School of Electronic Engineering, Xidian University, Xi'an 710071, ChinaGraph convolutional networks (GCNs) have been successfully applied to node representation learning in various real-world applications. However, the performance of GCNs drops rapidly when the labeled data are severely scarce, and the node features are prone to being indistinguishable with stacking more layers, causing over-fitting and over-smoothing problems. In this paper, we propose a simple yet effective contrastive semantic calibration for graph convolution network (CSC-GCN), which integrates stochastic identity aggregation and semantic calibration to overcome these weaknesses. The basic idea is the node features obtained from different aggregation operations should be similar. Toward that end, identity aggregation is utilized to extract semantic features from labeled nodes, while stochastic label noise is adopted to alleviate the over-fitting problem. Then, contrastive learning is employed to improve the discriminative ability of the node features, and the features from different aggregation operations are calibrated according to the class center similarity. In this way, the similarity between unlabeled features and labeled ones from the same class is enhanced while effectively reducing the over-smoothing problem. Experimental results on eight popular datasets show that the proposed CSC-GCN outperforms state-of-the-art methods on various classification tasks.http://www.sciencedirect.com/science/article/pii/S2949715923000598Graph convolution networkContrastive learningSemi-supervised learning |
| spellingShingle | Xu Yang Kun Wei Cheng Deng CSC-GCN: Contrastive semantic calibration for graph convolution network Journal of Information and Intelligence Graph convolution network Contrastive learning Semi-supervised learning |
| title | CSC-GCN: Contrastive semantic calibration for graph convolution network |
| title_full | CSC-GCN: Contrastive semantic calibration for graph convolution network |
| title_fullStr | CSC-GCN: Contrastive semantic calibration for graph convolution network |
| title_full_unstemmed | CSC-GCN: Contrastive semantic calibration for graph convolution network |
| title_short | CSC-GCN: Contrastive semantic calibration for graph convolution network |
| title_sort | csc gcn contrastive semantic calibration for graph convolution network |
| topic | Graph convolution network Contrastive learning Semi-supervised learning |
| url | http://www.sciencedirect.com/science/article/pii/S2949715923000598 |
| work_keys_str_mv | AT xuyang cscgcncontrastivesemanticcalibrationforgraphconvolutionnetwork AT kunwei cscgcncontrastivesemanticcalibrationforgraphconvolutionnetwork AT chengdeng cscgcncontrastivesemanticcalibrationforgraphconvolutionnetwork |