Multi-level disentanglement graph neural network.
Saved in:
| Title: | Multi-level disentanglement graph neural network. |
|---|---|
| Authors: | Wu, Lirong1,2 (AUTHOR) wulirong@westlake.edu.cn, Lin, Haitao2 (AUTHOR), Xia, Jun2 (AUTHOR), Tan, Cheng2 (AUTHOR), Li, Stan Z.2 (AUTHOR) |
| Source: | Neural Computing & Applications. Jun2022, Vol. 34 Issue 11, p9087-9101. 15p. |
| Subjects: | Deep learning, Diffusion processes |
| Abstract: | Real-world graphs are generally generated from highly entangled latent factors. However, existing deep learning methods for graph-structured data often ignore such entanglement and simply denote the heterogeneous relations between entities as binary edges. In this paper, we propose a novel Multi-level Disentanglement Graph Neural Network (MD-GNN), a unified framework that simultaneously implements edge-level, attribute-level, and node-level disentanglement in an end-to-end manner. MD-GNN takes the original graph structure and node attributes as input and outputs multiple disentangled relation graphs and disentangled node representations. Specifically, MD-GNN first disentangles the original graph structure into multiple relation graphs, each of which corresponds to a latent and disentangled relation among entities. The input node attributes are then propagated in the corresponding relation graph through a multi-hop diffusion mechanism to capture long-range dependencies between entities, and finally the disentangled node representations are obtained through information aggregation and merging. Extensive experiments on synthetic and real-world datasets have shown qualitatively and quantitatively that MD-GNN yields truly encouraging results in terms of disentanglement and also serves well as a general GNN framework for downstream tasks. Code has been made available at: https://github.com/LirongWu/MD-GNN. [ABSTRACT FROM AUTHOR] |
| Copyright of Neural Computing & Applications 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 | Links: – Type: pdflink Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 156859372 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Multi-level disentanglement graph neural network. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wu%2C+Lirong%22">Wu, Lirong</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> wulirong@westlake.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Lin%2C+Haitao%22">Lin, Haitao</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xia%2C+Jun%22">Xia, Jun</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tan%2C+Cheng%22">Tan, Cheng</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Stan+Z%2E%22">Li, Stan Z.</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computing+%26+Applications%22">Neural Computing & Applications</searchLink>. Jun2022, Vol. 34 Issue 11, p9087-9101. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Diffusion+processes%22">Diffusion processes</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Real-world graphs are generally generated from highly entangled latent factors. However, existing deep learning methods for graph-structured data often ignore such entanglement and simply denote the heterogeneous relations between entities as binary edges. In this paper, we propose a novel Multi-level Disentanglement Graph Neural Network (MD-GNN), a unified framework that simultaneously implements edge-level, attribute-level, and node-level disentanglement in an end-to-end manner. MD-GNN takes the original graph structure and node attributes as input and outputs multiple disentangled relation graphs and disentangled node representations. Specifically, MD-GNN first disentangles the original graph structure into multiple relation graphs, each of which corresponds to a latent and disentangled relation among entities. The input node attributes are then propagated in the corresponding relation graph through a multi-hop diffusion mechanism to capture long-range dependencies between entities, and finally the disentangled node representations are obtained through information aggregation and merging. Extensive experiments on synthetic and real-world datasets have shown qualitatively and quantitatively that MD-GNN yields truly encouraging results in terms of disentanglement and also serves well as a general GNN framework for downstream tasks. Code has been made available at: https://github.com/LirongWu/MD-GNN. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Neural Computing & Applications 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=156859372 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00521-022-06930-1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 9087 Subjects: – SubjectFull: Deep learning Type: general – SubjectFull: Diffusion processes Type: general Titles: – TitleFull: Multi-level disentanglement graph neural network. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wu, Lirong – PersonEntity: Name: NameFull: Lin, Haitao – PersonEntity: Name: NameFull: Xia, Jun – PersonEntity: Name: NameFull: Tan, Cheng – PersonEntity: Name: NameFull: Li, Stan Z. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 09410643 Numbering: – Type: volume Value: 34 – Type: issue Value: 11 Titles: – TitleFull: Neural Computing & Applications Type: main |
| ResultId | 1 |
