RaDE+: A semantic rank-based graph embedding algorithm
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.jjimei.2022.100078 http://hdl.handle.net/11449/241880 |
Resumo: | Due to the possibility of capturing complex relationships existing between nodes, many applications benefit from being modeled with graphs. However, performance issues can be observed in large-scale networks, making it computationally unfeasible to process in various scenarios. Graph Embedding methods emerge as a promising solution for finding low-dimensional vector representations for graphs, preserving their original properties such as topological characteristics, affinity, and shared neighborhood between nodes. Based on the vectorial representations, retrieval and machine learning techniques can be exploited to execute tasks such as classification, clustering, and link prediction. In this work, we propose RaDE (Rank Diffusion Embedding), an effective and efficient approach that considers rank-based graphs and representative nodes selection for learning a low-dimensional vector. We also present RaDE+, a variant that considers multiple representative nodes for more robust representations. The proposed approach was evaluated on 8 network datasets, including social, co-reference, textual, and image networks, with different properties. Vector representations generated with RaDE achieved effective results in visualization and retrieval tasks when compared to vector representations generated by other recent related methods. |
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oai:repositorio.unesp.br:11449/241880 |
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Repositório Institucional da UNESP |
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2946 |
spelling |
RaDE+: A semantic rank-based graph embedding algorithmDiffusionGraph embeddingInterpretabilityNetwork representation learningRankingSemanticUnsupervisedDue to the possibility of capturing complex relationships existing between nodes, many applications benefit from being modeled with graphs. However, performance issues can be observed in large-scale networks, making it computationally unfeasible to process in various scenarios. Graph Embedding methods emerge as a promising solution for finding low-dimensional vector representations for graphs, preserving their original properties such as topological characteristics, affinity, and shared neighborhood between nodes. Based on the vectorial representations, retrieval and machine learning techniques can be exploited to execute tasks such as classification, clustering, and link prediction. In this work, we propose RaDE (Rank Diffusion Embedding), an effective and efficient approach that considers rank-based graphs and representative nodes selection for learning a low-dimensional vector. We also present RaDE+, a variant that considers multiple representative nodes for more robust representations. The proposed approach was evaluated on 8 network datasets, including social, co-reference, textual, and image networks, with different properties. Vector representations generated with RaDE achieved effective results in visualization and retrieval tasks when compared to vector representations generated by other recent related methods.UNESP: Universidade Estadual Paulista Julio de Mesquita Filho LimeiraUNESP: Universidade Estadual Paulista Julio de Mesquita Filho LimeiraUniversidade Estadual Paulista (UNESP)de Fernando, Filipe Alves [UNESP]Pedronette, Daniel Carlos Guimarães [UNESP]de Sousa, Gustavo José [UNESP]Valem, Lucas Pascotti [UNESP]Guilherme, Ivan Rizzo [UNESP]2023-03-02T02:49:45Z2023-03-02T02:49:45Z2022-04-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.jjimei.2022.100078International Journal of Information Management Data Insights, v. 2, n. 1, 2022.2667-0968http://hdl.handle.net/11449/24188010.1016/j.jjimei.2022.1000782-s2.0-85130753178Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengInternational Journal of Information Management Data Insightsinfo:eu-repo/semantics/openAccess2023-03-02T02:49:46Zoai:repositorio.unesp.br:11449/241880Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:59:17.862697Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
RaDE+: A semantic rank-based graph embedding algorithm |
title |
RaDE+: A semantic rank-based graph embedding algorithm |
spellingShingle |
RaDE+: A semantic rank-based graph embedding algorithm de Fernando, Filipe Alves [UNESP] Diffusion Graph embedding Interpretability Network representation learning Ranking Semantic Unsupervised |
title_short |
RaDE+: A semantic rank-based graph embedding algorithm |
title_full |
RaDE+: A semantic rank-based graph embedding algorithm |
title_fullStr |
RaDE+: A semantic rank-based graph embedding algorithm |
title_full_unstemmed |
RaDE+: A semantic rank-based graph embedding algorithm |
title_sort |
RaDE+: A semantic rank-based graph embedding algorithm |
author |
de Fernando, Filipe Alves [UNESP] |
author_facet |
de Fernando, Filipe Alves [UNESP] Pedronette, Daniel Carlos Guimarães [UNESP] de Sousa, Gustavo José [UNESP] Valem, Lucas Pascotti [UNESP] Guilherme, Ivan Rizzo [UNESP] |
author_role |
author |
author2 |
Pedronette, Daniel Carlos Guimarães [UNESP] de Sousa, Gustavo José [UNESP] Valem, Lucas Pascotti [UNESP] Guilherme, Ivan Rizzo [UNESP] |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
de Fernando, Filipe Alves [UNESP] Pedronette, Daniel Carlos Guimarães [UNESP] de Sousa, Gustavo José [UNESP] Valem, Lucas Pascotti [UNESP] Guilherme, Ivan Rizzo [UNESP] |
dc.subject.por.fl_str_mv |
Diffusion Graph embedding Interpretability Network representation learning Ranking Semantic Unsupervised |
topic |
Diffusion Graph embedding Interpretability Network representation learning Ranking Semantic Unsupervised |
description |
Due to the possibility of capturing complex relationships existing between nodes, many applications benefit from being modeled with graphs. However, performance issues can be observed in large-scale networks, making it computationally unfeasible to process in various scenarios. Graph Embedding methods emerge as a promising solution for finding low-dimensional vector representations for graphs, preserving their original properties such as topological characteristics, affinity, and shared neighborhood between nodes. Based on the vectorial representations, retrieval and machine learning techniques can be exploited to execute tasks such as classification, clustering, and link prediction. In this work, we propose RaDE (Rank Diffusion Embedding), an effective and efficient approach that considers rank-based graphs and representative nodes selection for learning a low-dimensional vector. We also present RaDE+, a variant that considers multiple representative nodes for more robust representations. The proposed approach was evaluated on 8 network datasets, including social, co-reference, textual, and image networks, with different properties. Vector representations generated with RaDE achieved effective results in visualization and retrieval tasks when compared to vector representations generated by other recent related methods. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-04-01 2023-03-02T02:49:45Z 2023-03-02T02:49:45Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1016/j.jjimei.2022.100078 International Journal of Information Management Data Insights, v. 2, n. 1, 2022. 2667-0968 http://hdl.handle.net/11449/241880 10.1016/j.jjimei.2022.100078 2-s2.0-85130753178 |
url |
http://dx.doi.org/10.1016/j.jjimei.2022.100078 http://hdl.handle.net/11449/241880 |
identifier_str_mv |
International Journal of Information Management Data Insights, v. 2, n. 1, 2022. 2667-0968 10.1016/j.jjimei.2022.100078 2-s2.0-85130753178 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
International Journal of Information Management Data Insights |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129146641973248 |