Joint embedding of structure and features via graph convolutional networks

to related objects

Location: Deutsche Nationalbibliothek Frankfurt am Main

ISSN: 2364-8228

Extent: Online-Ressource

Language: Englisch

Notes: online resource.

Bibliographic citation: Joint embedding of structure and features via graph convolutional networks ; volume:5 ; number:1 ; day:9 ; month:1 ; year:2020 ; pages:1-24 ; date:12.2020
Applied network science ; 5, Heft 1 (9.1.2020), 1-24, 12.2020

Creator: Lerique, Sébastien
Abitbol, Jacob Levy
Karsai, Márton

Contributor: SpringerLink (Online service)

DOI: 10.1007/s41109-019-0237-x

URN: urn:nbn:de:101:1-2020041502434619184569

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 14.08.2025, 10:55 AM CEST

Data provider

This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.

Show original at data provider

Associated

Lerique, Sébastien
Abitbol, Jacob Levy
Karsai, Márton
SpringerLink (Online service)

Other Objects (12)

EOESGC: predicting miRNA-disease associations based on embedding of embedding and simplified graph convolutional network

Co-embedding of edges and nodes with deep graph convolutional neural networks

Relation path feature embedding based convolutional neural network method for drug discovery

Expanded graph embedding for joint network alignment and link prediction

ETGPDA: identification of piRNA-disease associations based on embedding transformation graph convolutional network

Embedding of complete graphs in broken Chimera graphs

AAGCN: a graph convolutional neural network with adaptive feature and topology learning

Applying Joint Graph Embedding to Study Alzheimer’s Neurodegeneration Patterns in Volumetric Data

Relation path embedding in knowledge graphs

A survey on bipartite graphs embedding

Temporal Knowledge Graph Embedding and Reasoning

Knowledge graph embedding closed under composition

EOESGC: predicting miRNA-disease associations based on embedding of embedding and simplified graph convolutional network

Co-embedding of edges and nodes with deep graph convolutional neural networks

Relation path feature embedding based convolutional neural network method for drug discovery

Expanded graph embedding for joint network alignment and link prediction

ETGPDA: identification of piRNA-disease associations based on embedding transformation graph convolutional network

Embedding of complete graphs in broken Chimera graphs

AAGCN: a graph convolutional neural network with adaptive feature and topology learning

Applying Joint Graph Embedding to Study Alzheimer’s Neurodegeneration Patterns in Volumetric Data

Relation path embedding in knowledge graphs

A survey on bipartite graphs embedding

Temporal Knowledge Graph Embedding and Reasoning

Knowledge graph embedding closed under composition

EOESGC: predicting miRNA-disease associations based on embedding of embedding and simplified graph convolutional network

Co-embedding of edges and nodes with deep graph convolutional neural networks

Relation path feature embedding based convolutional neural network method for drug discovery

Expanded graph embedding for joint network alignment and link prediction

ETGPDA: identification of piRNA-disease associations based on embedding transformation graph convolutional network

Embedding of complete graphs in broken Chimera graphs

AAGCN: a graph convolutional neural network with adaptive feature and topology learning

Applying Joint Graph Embedding to Study Alzheimer’s Neurodegeneration Patterns in Volumetric Data

Relation path embedding in knowledge graphs

A survey on bipartite graphs embedding

Temporal Knowledge Graph Embedding and Reasoning

Knowledge graph embedding closed under composition

Cultural heritage institutions wishing to register will find more information here.

Fields marked * need to be filled in.

Username*

Please enter your username

Email*

Please enter your email address

Please do not fill this field

First name

Last name

Password*

Please enter your password

Confirm password*

Please enter the same password

I have read the terms of use and the privacy policy for the collection of personal data and accept them. *

This field is required.

I would like to subscribe to the newsletter of the Deutsche Digitale Bibliothek. See newsletter subscription info.

Account created

Your "My DDB" account has been successfully created. Before you can log in to your account, you must click the confirmation link in the message we just sent to the email address you provided.

Joint embedding of structure and features via graph convolutional networks

Object Details

References and Relationships

Contributors, Places and Time

Further information

Data provider

Associated

Other Objects (12)

EOESGC: predicting miRNA-disease associations based on embedding of embedding and simplified graph convolutional network

Co-embedding of edges and nodes with deep graph convolutional neural networks

Relation path feature embedding based convolutional neural network method for drug discovery

Expanded graph embedding for joint network alignment and link prediction

ETGPDA: identification of piRNA-disease associations based on embedding transformation graph convolutional network

Embedding of complete graphs in broken Chimera graphs

AAGCN: a graph convolutional neural network with adaptive feature and topology learning

Applying Joint Graph Embedding to Study Alzheimer’s Neurodegeneration Patterns in Volumetric Data

Relation path embedding in knowledge graphs

A survey on bipartite graphs embedding

Temporal Knowledge Graph Embedding and Reasoning

Knowledge graph embedding closed under composition

EOESGC: predicting miRNA-disease associations based on embedding of embedding and simplified graph convolutional network

Co-embedding of edges and nodes with deep graph convolutional neural networks

Relation path feature embedding based convolutional neural network method for drug discovery

Expanded graph embedding for joint network alignment and link prediction

ETGPDA: identification of piRNA-disease associations based on embedding transformation graph convolutional network

Embedding of complete graphs in broken Chimera graphs

AAGCN: a graph convolutional neural network with adaptive feature and topology learning

Applying Joint Graph Embedding to Study Alzheimer’s Neurodegeneration Patterns in Volumetric Data

Relation path embedding in knowledge graphs

A survey on bipartite graphs embedding

Temporal Knowledge Graph Embedding and Reasoning

Knowledge graph embedding closed under composition

EOESGC: predicting miRNA-disease associations based on embedding of embedding and simplified graph convolutional network

Co-embedding of edges and nodes with deep graph convolutional neural networks

Relation path feature embedding based convolutional neural network method for drug discovery

Expanded graph embedding for joint network alignment and link prediction

ETGPDA: identification of piRNA-disease associations based on embedding transformation graph convolutional network

Embedding of complete graphs in broken Chimera graphs

AAGCN: a graph convolutional neural network with adaptive feature and topology learning

Applying Joint Graph Embedding to Study Alzheimer’s Neurodegeneration Patterns in Volumetric Data

Relation path embedding in knowledge graphs

A survey on bipartite graphs embedding

Temporal Knowledge Graph Embedding and Reasoning

Knowledge graph embedding closed under composition

Related objects

Reset password