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Relationship Discovery and Hierarchical Embedding for Web Service Quality Prediction

Web Services Quality Prediction has become a popular research theme in Cloud Computing and the Internet of Things. Graph Convolutional Network (GCN)-based methods are more efficient by aggregating feature information from the local graph neighborhood. Despite the fact that these prior works have dem...

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Detalles Bibliográficos
Autores principales: Bu, Hualong, Xia, Jing, Wu, Qilin, Chen, Liping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9556205/
https://www.ncbi.nlm.nih.gov/pubmed/36248925
http://dx.doi.org/10.1155/2022/9240843
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author Bu, Hualong
Xia, Jing
Wu, Qilin
Chen, Liping
author_facet Bu, Hualong
Xia, Jing
Wu, Qilin
Chen, Liping
author_sort Bu, Hualong
collection PubMed
description Web Services Quality Prediction has become a popular research theme in Cloud Computing and the Internet of Things. Graph Convolutional Network (GCN)-based methods are more efficient by aggregating feature information from the local graph neighborhood. Despite the fact that these prior works have demonstrated better prediction performance, they are still challenged as follows: (1) first, the user-service bipartite graph is essentially a heterogeneous graph that contains four kinds of relationships. Previous GCN-based models have only focused on using some of these relationships. Therefore, how to fully mine and use the above relationships is critical to improving the prediction accuracy. (2) After the embedding is obtained from the GCNs, the commonly used similarity calculation methods for downstream prediction need to traverse the data one by one, which is time-consuming. To address these challenges, this work proposes a novel relationship discovery and hierarchical embedding method based on GCNs (named as RDHE), which designs a dual mechanism to represent services and users, respectively, designs a new community discovery method and a fast similarity calculation process, which can fully mine and utilize the relationships in the graph. The results of the experiment on the real data set show that this method greatly improved the accuracy of the web service quality prediction.
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spelling pubmed-95562052022-10-13 Relationship Discovery and Hierarchical Embedding for Web Service Quality Prediction Bu, Hualong Xia, Jing Wu, Qilin Chen, Liping Comput Intell Neurosci Research Article Web Services Quality Prediction has become a popular research theme in Cloud Computing and the Internet of Things. Graph Convolutional Network (GCN)-based methods are more efficient by aggregating feature information from the local graph neighborhood. Despite the fact that these prior works have demonstrated better prediction performance, they are still challenged as follows: (1) first, the user-service bipartite graph is essentially a heterogeneous graph that contains four kinds of relationships. Previous GCN-based models have only focused on using some of these relationships. Therefore, how to fully mine and use the above relationships is critical to improving the prediction accuracy. (2) After the embedding is obtained from the GCNs, the commonly used similarity calculation methods for downstream prediction need to traverse the data one by one, which is time-consuming. To address these challenges, this work proposes a novel relationship discovery and hierarchical embedding method based on GCNs (named as RDHE), which designs a dual mechanism to represent services and users, respectively, designs a new community discovery method and a fast similarity calculation process, which can fully mine and utilize the relationships in the graph. The results of the experiment on the real data set show that this method greatly improved the accuracy of the web service quality prediction. Hindawi 2022-10-05 /pmc/articles/PMC9556205/ /pubmed/36248925 http://dx.doi.org/10.1155/2022/9240843 Text en Copyright © 2022 Hualong Bu et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Bu, Hualong
Xia, Jing
Wu, Qilin
Chen, Liping
Relationship Discovery and Hierarchical Embedding for Web Service Quality Prediction
title Relationship Discovery and Hierarchical Embedding for Web Service Quality Prediction
title_full Relationship Discovery and Hierarchical Embedding for Web Service Quality Prediction
title_fullStr Relationship Discovery and Hierarchical Embedding for Web Service Quality Prediction
title_full_unstemmed Relationship Discovery and Hierarchical Embedding for Web Service Quality Prediction
title_short Relationship Discovery and Hierarchical Embedding for Web Service Quality Prediction
title_sort relationship discovery and hierarchical embedding for web service quality prediction
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9556205/
https://www.ncbi.nlm.nih.gov/pubmed/36248925
http://dx.doi.org/10.1155/2022/9240843
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