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Conference Paper: Structural Role Enhanced Attributed Network Embedding

TitleStructural Role Enhanced Attributed Network Embedding
Authors
KeywordsAttributed network
Autoencoder
Network embedding
Structural role proximity
Issue Date2019
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, v. 11881 LNCS, p. 568-582 How to Cite?
AbstractIn recent years, network embedding methods based on deep learning to process network structure data have attracted widespread attention. It aims to represent nodes in the network as low-dimensional dense real-value vectors and effectively preserve network structure and other valuable information. Most network embedding methods now only preserve the network topology and do not take advantage of the rich attribute information in networks. In this paper, we propose a novel deep attributed network embedding framework (RolEANE), which can preserve network topological structure and attribute information well at the same time. The framework consists of two parts, one of which is the network structural role proximity enhanced deep autoencoder, which is used to capture highly nonlinear network topological structure and attribute information. The other part is that we proposed a neighbor optimization strategy to modify the Skip-Gram model so that it can integrate the network topological structure and attribute information to improve the final embedded performance. The experiments on four real datasets show that our method outperforms other state-of-the-art network embedding methods.
Persistent Identifierhttp://hdl.handle.net/10722/330626
ISSN
2023 SCImago Journal Rankings: 0.606
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLi, Zhao-
dc.contributor.authorWang, Xin-
dc.contributor.authorLi, Jianxin-
dc.contributor.authorZhang, Qingpeng-
dc.date.accessioned2023-09-05T12:12:27Z-
dc.date.available2023-09-05T12:12:27Z-
dc.date.issued2019-
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, v. 11881 LNCS, p. 568-582-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/330626-
dc.description.abstractIn recent years, network embedding methods based on deep learning to process network structure data have attracted widespread attention. It aims to represent nodes in the network as low-dimensional dense real-value vectors and effectively preserve network structure and other valuable information. Most network embedding methods now only preserve the network topology and do not take advantage of the rich attribute information in networks. In this paper, we propose a novel deep attributed network embedding framework (RolEANE), which can preserve network topological structure and attribute information well at the same time. The framework consists of two parts, one of which is the network structural role proximity enhanced deep autoencoder, which is used to capture highly nonlinear network topological structure and attribute information. The other part is that we proposed a neighbor optimization strategy to modify the Skip-Gram model so that it can integrate the network topological structure and attribute information to improve the final embedded performance. The experiments on four real datasets show that our method outperforms other state-of-the-art network embedding methods.-
dc.languageeng-
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.subjectAttributed network-
dc.subjectAutoencoder-
dc.subjectNetwork embedding-
dc.subjectStructural role proximity-
dc.titleStructural Role Enhanced Attributed Network Embedding-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-030-34223-4_36-
dc.identifier.scopuseid_2-s2.0-85076958267-
dc.identifier.volume11881 LNCS-
dc.identifier.spage568-
dc.identifier.epage582-
dc.identifier.eissn1611-3349-
dc.identifier.isiWOS:000611516600036-

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