Deep Semantic Network Representation

Luo, X; Wu, J; Zhou, C; Zhang, X; Wang, Y

Deep Semantic Network Representation

Luo, X Wu, J Zhou, C Zhang, X Wang, Y

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2020 IEEE International Conference on Data Mining (ICDM), 2021, 2020-November, pp. 1154-1159
Issue Date:: 2021-02-09

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Field	Value	Language
dc.contributor.author	Luo, X
dc.contributor.author	Wu, J
dc.contributor.author	Zhou, C
dc.contributor.author	Zhang, X
dc.contributor.author	Wang, Y
dc.date	2020-11-17
dc.date.accessioned	2022-06-11T00:07:33Z
dc.date.available	2022-06-11T00:07:33Z
dc.date.issued	2021-02-09
dc.identifier.citation	2020 IEEE International Conference on Data Mining (ICDM), 2021, 2020-November, pp. 1154-1159
dc.identifier.isbn	9781728183169
dc.identifier.issn	1550-4786
dc.identifier.uri	http://hdl.handle.net/10453/158086
dc.description.abstract	Network representation aims to learn low-dimensional vector representations of network nodes while preserving the inherent properties of the network. For all its popularity, majority of the existing methods focus on exploitation of diverse information, including network topology and semantic information on nodes of network, and ignore their implicit semantics. For example, we all know the saying that birds of a feather flock together. More concretely, semantic information of one node can be influenced by its neighbors' semantic information. Furthermore, even two nodes are not directly connected, they may have similar implicit semantic information (i.e., high-order semantic proximity). Thus, they should be close in the represented vector space. To this end, we propose a Deep Semantic Network Representation approach (DSNR) in the self-translation framework from sequence to sequence. To excavate the implicit semantic information of nodes and capture the high-order semantic proximity, three key components make our approach effective, i.e., aggregation of nodes neighbors' semantic information and enhancement to the semantic feature representations of nodes by a deep autoencoder, integration of nodes semantic information in node identity sequence to generate node semantic sequence, and translation from node semantic sequence to node identity sequence to capture the high-order semantic proximity in an attention-enhanced seq2seq framework. Extensive experiments based on three real-world datasets have verified the effectiveness of our proposed approach11Code is available at https://github.com/DASE4/DSNR.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2020 IEEE International Conference on Data Mining (ICDM)
dc.relation.ispartof	2020 IEEE International Conference on Data Mining
dc.relation.isbasedon	10.1109/icdm50108.2020.00141
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Deep Semantic Network Representation
dc.type	Conference Proceeding
utslib.citation.volume	2020-November
utslib.location.activity	Sorrento, Italy
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-06-11T00:07:32Z
pubs.finish-date	2020-11-20
pubs.publication-status	Published
pubs.start-date	2020-11-17
pubs.volume	2020-November

Abstract:

Network representation aims to learn low-dimensional vector representations of network nodes while preserving the inherent properties of the network. For all its popularity, majority of the existing methods focus on exploitation of diverse information, including network topology and semantic information on nodes of network, and ignore their implicit semantics. For example, we all know the saying that birds of a feather flock together. More concretely, semantic information of one node can be influenced by its neighbors' semantic information. Furthermore, even two nodes are not directly connected, they may have similar implicit semantic information (i.e., high-order semantic proximity). Thus, they should be close in the represented vector space. To this end, we propose a Deep Semantic Network Representation approach (DSNR) in the self-translation framework from sequence to sequence. To excavate the implicit semantic information of nodes and capture the high-order semantic proximity, three key components make our approach effective, i.e., aggregation of nodes neighbors' semantic information and enhancement to the semantic feature representations of nodes by a deep autoencoder, integration of nodes semantic information in node identity sequence to generate node semantic sequence, and translation from node semantic sequence to node identity sequence to capture the high-order semantic proximity in an attention-enhanced seq2seq framework. Extensive experiments based on three real-world datasets have verified the effectiveness of our proposed approach11Code is available at https://github.com/DASE4/DSNR.

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http://hdl.handle.net/10453/158086