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Individuality-enhanced and multi-granularity consistency-preserving graph neural network for semi-supervised node classification
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Liu, Xinxin and Yu, Weiren (2023) Individuality-enhanced and multi-granularity consistency-preserving graph neural network for semi-supervised node classification. Applied Intelligence, 53 . pp. 27608-27623. doi:10.1007/s10489-023-04974-x ISSN 0924-669X.
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Official URL: http://doi.org/10.1007/s10489-023-04974-x
Abstract
Semi-supervised node classification is an important task that aims at classifying nodes based on the graph structure, node features, and class labels for a subset of nodes. While most graph convolutional networks (GCNs) perform well when an ample number of labeled nodes are available, they often degenerate when the amount of labeled data is limited. To address this problem, we propose a scheme, namely, Individuality-enhanced and Multi-granularity Consistency-preserving graph neural Network (IMCN), which can alleviate the problem of losing individual information within the encoder while providing a reliable supervised signal for learning purposes. First, one simple encoder based on node features only is integrated to enhance node individuality and amend node commonality learned by the GCN-based encoder. Then, three constraints are defined at different levels of granularity, encompassing node embedding agreement, semantic class alignment, and node-to-class distribution identity. They can maintain the consistency between the individuality and commonality of nodes and be leveraged as latent supervised signals for learning representative embeddings. Finally, the trade-off between the individuality and commonality of nodes captured by two encoders is taken into consideration for node classification. Extensive experiments on six real-world datasets have been conducted to validate the superiority of IMCN against state-of-the-art baselines in handling node classification tasks with scarce labeled data.
Item Type: | Journal Article | ||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||
Library of Congress Subject Headings (LCSH): | Neural networks (Computer science), Graph theory, Deep learning (Machine learning), Graph theory -- Data processing, Supervised learning (Machine learning), Wireless localization | ||||||||
Journal or Publication Title: | Applied Intelligence | ||||||||
Publisher: | Springer New York LLC | ||||||||
ISSN: | 0924-669X | ||||||||
Official Date: | November 2023 | ||||||||
Dates: |
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Volume: | 53 | ||||||||
Page Range: | pp. 27608-27623 | ||||||||
DOI: | 10.1007/s10489-023-04974-x | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||
Date of first compliant deposit: | 18 September 2023 | ||||||||
Date of first compliant Open Access: | 18 September 2023 | ||||||||
RIOXX Funder/Project Grant: |
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