Nonlinear Graph Learning-Convolutional Networks for Node Classification.
Saved in:
| Title: | Nonlinear Graph Learning-Convolutional Networks for Node Classification. |
|---|---|
| Authors: | Chen, Linjun1 (AUTHOR), Liu, Xingyi1,2 (AUTHOR) 973575386@qq.com, Li, Zexin1 (AUTHOR) |
| Source: | Neural Processing Letters. Aug2022, Vol. 54 Issue 4, p2727-2736. 10p. |
| Subjects: | Representations of graphs, Feature selection, Classification |
| Abstract: | Graph Convolutional Networks have been widely used for node classification. Since the original data usually contains nonlinear relationships that are difficult to capture and includes noise that leads to the poor performance of the constructed graph representation, the paper proposes a novel Nonlinear Graph Learning-Convolutional Network (NGLCN) based on the kernel method and graph representation learning. Specifically, NGLCN first uses a kernel method to map the original data into kernel space, making the original linearly separable to capture the nonlinear relationship between the data, and then uses a feature selection based on structure information to remove the noisy and redundant feature and constructs a high-quality graph representation, and finally employs a common graph convolutional network to conduct node classification tasks. Experimental results on eight benchmark datasets show that NGLCN outperforms the state-of-the-art traditional graph convolutional networks. [ABSTRACT FROM AUTHOR] |
| Copyright of Neural Processing Letters is the property of Springer Nature 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 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 158485370 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Nonlinear Graph Learning-Convolutional Networks for Node Classification. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chen%2C+Linjun%22">Chen, Linjun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Xingyi%22">Liu, Xingyi</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> 973575386@qq.com</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Zexin%22">Li, Zexin</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Processing+Letters%22">Neural Processing Letters</searchLink>. Aug2022, Vol. 54 Issue 4, p2727-2736. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Representations+of+graphs%22">Representations of graphs</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Graph Convolutional Networks have been widely used for node classification. Since the original data usually contains nonlinear relationships that are difficult to capture and includes noise that leads to the poor performance of the constructed graph representation, the paper proposes a novel Nonlinear Graph Learning-Convolutional Network (NGLCN) based on the kernel method and graph representation learning. Specifically, NGLCN first uses a kernel method to map the original data into kernel space, making the original linearly separable to capture the nonlinear relationship between the data, and then uses a feature selection based on structure information to remove the noisy and redundant feature and constructs a high-quality graph representation, and finally employs a common graph convolutional network to conduct node classification tasks. Experimental results on eight benchmark datasets show that NGLCN outperforms the state-of-the-art traditional graph convolutional networks. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Neural Processing Letters is the property of Springer Nature 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=158485370 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11063-021-10478-x Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 2727 Subjects: – SubjectFull: Representations of graphs Type: general – SubjectFull: Feature selection Type: general – SubjectFull: Classification Type: general Titles: – TitleFull: Nonlinear Graph Learning-Convolutional Networks for Node Classification. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chen, Linjun – PersonEntity: Name: NameFull: Liu, Xingyi – PersonEntity: Name: NameFull: Li, Zexin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 13704621 Numbering: – Type: volume Value: 54 – Type: issue Value: 4 Titles: – TitleFull: Neural Processing Letters Type: main |
| ResultId | 1 |
