Hill Climbing-Based Efficient Model for Link Prediction in Undirected Graphs

Detalhes bibliográficos
Autor(a) principal: Gul, Haji
Data de Publicação: 2022
Outros Autores: Al-Obeidat, Feras, Amin, Adnan, Moreira, Fernando, Huang, Kaizhu
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/11328/4624
https://doi.org/10.3390/math10224265
Resumo: Link prediction is a key problem in the field of undirected graph, and it can be used in a variety of contexts, including information retrieval and market analysis. By “undirected graphs”, we mean undirected complex networks in this study. The ability to predict new links in complex networks has a significant impact on society. Many complex systems can be modelled using networks. For example, links represent relationships (such as friendships, etc.) in social networks, whereas nodes represent users. Embedding methods, which produce the feature vector of each node in a graph and identify unknown links, are one of the newest approaches to link prediction. The Deep Walk algorithm is a common graph embedding approach that uses pure random walking to capture network structure. In this paper, we propose an efficient model for link prediction based on a hill climbing algorithm. It is used as a cost function. The lower the cost is, the higher the accuracy for link prediction between the source and destination node will be. Unlike other algorithms that predict links based on a single feature, it takes advantage of multiple features. The proposed method has been tested over nine publicly available datasets, and its performance has been evaluated by comparing it to other frequently used indexes. Our model outperforms all of these measures, as indicated by its higher prediction accuracy.
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spelling Hill Climbing-Based Efficient Model for Link Prediction in Undirected GraphsComplex network analysisLocal link prediction methodsLink predictionComplex networksHill climbingLink prediction is a key problem in the field of undirected graph, and it can be used in a variety of contexts, including information retrieval and market analysis. By “undirected graphs”, we mean undirected complex networks in this study. The ability to predict new links in complex networks has a significant impact on society. Many complex systems can be modelled using networks. For example, links represent relationships (such as friendships, etc.) in social networks, whereas nodes represent users. Embedding methods, which produce the feature vector of each node in a graph and identify unknown links, are one of the newest approaches to link prediction. The Deep Walk algorithm is a common graph embedding approach that uses pure random walking to capture network structure. In this paper, we propose an efficient model for link prediction based on a hill climbing algorithm. It is used as a cost function. The lower the cost is, the higher the accuracy for link prediction between the source and destination node will be. Unlike other algorithms that predict links based on a single feature, it takes advantage of multiple features. The proposed method has been tested over nine publicly available datasets, and its performance has been evaluated by comparing it to other frequently used indexes. Our model outperforms all of these measures, as indicated by its higher prediction accuracy.MDPI - Multidisciplinary Digital Publishing Institute2023-01-10T15:24:13Z2023-01-102022-11-15T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfGul, H., Al-Obeidat, F., Amin, A., Moreira, F., & Huang, K. (2022). Hill Climbing-Based Efficient Model for Link Prediction in Undirected Graphs. Mathematics, 10(Article ID 4265), 1-15. https://doi.org/10.3390/math10224265. Repositório Institucional UPT. http://hdl.handle.net/11328/4624http://hdl.handle.net/11328/4624Gul, H., Al-Obeidat, F., Amin, A., Moreira, F., & Huang, K. (2022). Hill Climbing-Based Efficient Model for Link Prediction in Undirected Graphs. Mathematics, 10(Article ID 4265), 1-15. https://doi.org/10.3390/math10224265. Repositório Institucional UPT. http://hdl.handle.net/11328/4624http://hdl.handle.net/11328/4624https://doi.org/10.3390/math10224265eng2227-7390 (Electronic)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessGul, HajiAl-Obeidat, FerasAmin, AdnanMoreira, FernandoHuang, Kaizhureponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-16T02:16:04Zoai:repositorio.upt.pt:11328/4624Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:42:20.669723Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Hill Climbing-Based Efficient Model for Link Prediction in Undirected Graphs
title Hill Climbing-Based Efficient Model for Link Prediction in Undirected Graphs
spellingShingle Hill Climbing-Based Efficient Model for Link Prediction in Undirected Graphs
Gul, Haji
Complex network analysis
Local link prediction methods
Link prediction
Complex networks
Hill climbing
title_short Hill Climbing-Based Efficient Model for Link Prediction in Undirected Graphs
title_full Hill Climbing-Based Efficient Model for Link Prediction in Undirected Graphs
title_fullStr Hill Climbing-Based Efficient Model for Link Prediction in Undirected Graphs
title_full_unstemmed Hill Climbing-Based Efficient Model for Link Prediction in Undirected Graphs
title_sort Hill Climbing-Based Efficient Model for Link Prediction in Undirected Graphs
author Gul, Haji
author_facet Gul, Haji
Al-Obeidat, Feras
Amin, Adnan
Moreira, Fernando
Huang, Kaizhu
author_role author
author2 Al-Obeidat, Feras
Amin, Adnan
Moreira, Fernando
Huang, Kaizhu
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Gul, Haji
Al-Obeidat, Feras
Amin, Adnan
Moreira, Fernando
Huang, Kaizhu
dc.subject.por.fl_str_mv Complex network analysis
Local link prediction methods
Link prediction
Complex networks
Hill climbing
topic Complex network analysis
Local link prediction methods
Link prediction
Complex networks
Hill climbing
description Link prediction is a key problem in the field of undirected graph, and it can be used in a variety of contexts, including information retrieval and market analysis. By “undirected graphs”, we mean undirected complex networks in this study. The ability to predict new links in complex networks has a significant impact on society. Many complex systems can be modelled using networks. For example, links represent relationships (such as friendships, etc.) in social networks, whereas nodes represent users. Embedding methods, which produce the feature vector of each node in a graph and identify unknown links, are one of the newest approaches to link prediction. The Deep Walk algorithm is a common graph embedding approach that uses pure random walking to capture network structure. In this paper, we propose an efficient model for link prediction based on a hill climbing algorithm. It is used as a cost function. The lower the cost is, the higher the accuracy for link prediction between the source and destination node will be. Unlike other algorithms that predict links based on a single feature, it takes advantage of multiple features. The proposed method has been tested over nine publicly available datasets, and its performance has been evaluated by comparing it to other frequently used indexes. Our model outperforms all of these measures, as indicated by its higher prediction accuracy.
publishDate 2022
dc.date.none.fl_str_mv 2022-11-15T00:00:00Z
2023-01-10T15:24:13Z
2023-01-10
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 Gul, H., Al-Obeidat, F., Amin, A., Moreira, F., & Huang, K. (2022). Hill Climbing-Based Efficient Model for Link Prediction in Undirected Graphs. Mathematics, 10(Article ID 4265), 1-15. https://doi.org/10.3390/math10224265. Repositório Institucional UPT. http://hdl.handle.net/11328/4624
http://hdl.handle.net/11328/4624
Gul, H., Al-Obeidat, F., Amin, A., Moreira, F., & Huang, K. (2022). Hill Climbing-Based Efficient Model for Link Prediction in Undirected Graphs. Mathematics, 10(Article ID 4265), 1-15. https://doi.org/10.3390/math10224265. Repositório Institucional UPT. http://hdl.handle.net/11328/4624
http://hdl.handle.net/11328/4624
https://doi.org/10.3390/math10224265
identifier_str_mv Gul, H., Al-Obeidat, F., Amin, A., Moreira, F., & Huang, K. (2022). Hill Climbing-Based Efficient Model for Link Prediction in Undirected Graphs. Mathematics, 10(Article ID 4265), 1-15. https://doi.org/10.3390/math10224265. Repositório Institucional UPT. http://hdl.handle.net/11328/4624
url http://hdl.handle.net/11328/4624
https://doi.org/10.3390/math10224265
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2227-7390 (Electronic)
dc.rights.driver.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
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eu_rights_str_mv openAccess
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dc.publisher.none.fl_str_mv MDPI - Multidisciplinary Digital Publishing Institute
publisher.none.fl_str_mv MDPI - Multidisciplinary Digital Publishing Institute
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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