Adaptive receptive field graph neural networks.
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| Title: | Adaptive receptive field graph neural networks. |
|---|---|
| Authors: | Gao, Hepeng1 (AUTHOR) gaohepeng13@hotmail.com, Yang, Funing1 (AUTHOR) harptomato@163.com, Yang, Yongjian1 (AUTHOR) yyj@jlu.edu.cn, Xu, Yuanbo1 (AUTHOR) yuanbox@jlu.edu.cn, Su, Yijun2 (AUTHOR) suyijun.ucas@gmail.com |
| Source: | Neural Networks. Oct2025, Vol. 190, pN.PAG-N.PAG. 1p. |
| Subjects: | Graph neural networks, Learning ability, Monomolecular films, Classification |
| Abstract: | Graph Neural Networks (GNNs) have drawn increasing attention in recent years and achieved outstanding success in many scenarios and tasks. However, existing methods indicate that the performance of representation learning drops dramatically as GNNs deepen, which is attributed to over-smoothing representation. To handle the above issue, we propose an adaptive receptive field graph neural network (ADRP-GNN) that aggregates information by adaptively expanding receptive fields with a monolayer graph convolution layer, avoiding deepening to result in the over-smoothing issue. Specifically, we first present a Multi-hop Graph Convolution Network (MuGC) that captures the information of the nodes and their multi-hop neighbors with only one layer, preventing frequent passing messages between nodes from the over-smoothing issue. Then, we design a Meta Learner that realizes the adaptive receptive field for each node to select related neighbor information. Finally, a Backbone Network is employed to enhance the architecture's learning ability. In addition, our architecture adaptively generates receptive fields instead of handcrafting stacked layers, which can integrate existing GNN frameworks to fit various scenarios. Extensive experiments indicate that our architecture is effective for the over-smoothing issue and improves accuracy by 0.52% to 6.88% compared to state-of-the-art methods on node classification tasks on eight datasets. • A monolayer graph convolution (MuGC) captures multi-hop graph structure information. • Meta Learner realizes the adaptive receptive field for each node. • Our framework, ADRP-GNN, relieves the over-smoothing issue. • Extensive experiments are conducted to evaluate ADRP-GNN on 8 real-world datasets. [ABSTRACT FROM AUTHOR] |
| Copyright of Neural Networks is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 187115385 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Adaptive receptive field graph neural networks. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Gao%2C+Hepeng%22">Gao, Hepeng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> gaohepeng13@hotmail.com</i><br /><searchLink fieldCode="AR" term="%22Yang%2C+Funing%22">Yang, Funing</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> harptomato@163.com</i><br /><searchLink fieldCode="AR" term="%22Yang%2C+Yongjian%22">Yang, Yongjian</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> yyj@jlu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Xu%2C+Yuanbo%22">Xu, Yuanbo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> yuanbox@jlu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Su%2C+Yijun%22">Su, Yijun</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> suyijun.ucas@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Networks%22">Neural Networks</searchLink>. Oct2025, Vol. 190, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Graph+neural+networks%22">Graph neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+ability%22">Learning ability</searchLink><br /><searchLink fieldCode="DE" term="%22Monomolecular+films%22">Monomolecular films</searchLink><br /><searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Graph Neural Networks (GNNs) have drawn increasing attention in recent years and achieved outstanding success in many scenarios and tasks. However, existing methods indicate that the performance of representation learning drops dramatically as GNNs deepen, which is attributed to over-smoothing representation. To handle the above issue, we propose an adaptive receptive field graph neural network (ADRP-GNN) that aggregates information by adaptively expanding receptive fields with a monolayer graph convolution layer, avoiding deepening to result in the over-smoothing issue. Specifically, we first present a Multi-hop Graph Convolution Network (MuGC) that captures the information of the nodes and their multi-hop neighbors with only one layer, preventing frequent passing messages between nodes from the over-smoothing issue. Then, we design a Meta Learner that realizes the adaptive receptive field for each node to select related neighbor information. Finally, a Backbone Network is employed to enhance the architecture's learning ability. In addition, our architecture adaptively generates receptive fields instead of handcrafting stacked layers, which can integrate existing GNN frameworks to fit various scenarios. Extensive experiments indicate that our architecture is effective for the over-smoothing issue and improves accuracy by 0.52% to 6.88% compared to state-of-the-art methods on node classification tasks on eight datasets. • A monolayer graph convolution (MuGC) captures multi-hop graph structure information. • Meta Learner realizes the adaptive receptive field for each node. • Our framework, ADRP-GNN, relieves the over-smoothing issue. • Extensive experiments are conducted to evaluate ADRP-GNN on 8 real-world datasets. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Neural Networks is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.neunet.2025.107658 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Graph neural networks Type: general – SubjectFull: Learning ability Type: general – SubjectFull: Monomolecular films Type: general – SubjectFull: Classification Type: general Titles: – TitleFull: Adaptive receptive field graph neural networks. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Gao, Hepeng – PersonEntity: Name: NameFull: Yang, Funing – PersonEntity: Name: NameFull: Yang, Yongjian – PersonEntity: Name: NameFull: Xu, Yuanbo – PersonEntity: Name: NameFull: Su, Yijun IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 08936080 Numbering: – Type: volume Value: 190 Titles: – TitleFull: Neural Networks Type: main |
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