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Distribution Preserving Graph Representation Learning

Distribution Preserving Graph Representation Learning

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2634667278

Distribution Preserving Graph Representation Learning

About this item

Full title

Distribution Preserving Graph Representation Learning

Author / Creator

Publisher

Ithaca: Cornell University Library, arXiv.org

Journal title

arXiv.org, 2022-02

Language

English

Formats

Publication information

Publisher

Ithaca: Cornell University Library, arXiv.org

More information

Scope and Contents

Contents

Graph neural network (GNN) is effective to model graphs for distributed representations of nodes and an entire graph. Recently, research on the expressive power of GNN attracted growing attention. A highly-expressive GNN has the ability to generate discriminative graph representations. However, in the end-to-end training process for a certain graph...

Alternative Titles

Full title

Distribution Preserving Graph Representation Learning

Authors, Artists and Contributors

Identifiers

Primary Identifiers

Record Identifier

TN_cdi_proquest_journals_2634667278

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2634667278

Other Identifiers

E-ISSN

2331-8422

How to access this item