Hill Climbing-Based Efficient Model for Link Prediction in Undirected Graphs
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , |
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 |
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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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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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 Institute2023-01-10T15:24:13Z2022-11-15T00:00:00Z2022-11-15info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/11328/4624eng2227-7390 (Electronic)https://doi.org/10.3390/math10224265Gul, HajiAl-Obeidat, FerasAmin, AdnanMoreira, FernandoHuang, Kaizhuinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-06-15T02:13:19ZPortal AgregadorONG |
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 2022-11-15 2023-01-10T15:24:13Z |
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://hdl.handle.net/11328/4624 |
url |
http://hdl.handle.net/11328/4624 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2227-7390 (Electronic) https://doi.org/10.3390/math10224265 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
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) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
|
repository.mail.fl_str_mv |
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_version_ |
1777302558004477952 |