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Low-Complexity Hyperbolic Embedding Schemes for Temporal Complex Networks
Hyperbolic embedding can effectively preserve the property of complex networks. Though some state-of-the-art hyperbolic node embedding approaches are proposed, most of them are still not well suited for the dynamic evolution process of temporal complex networks. The complexities of the adaptability...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9736245/ https://www.ncbi.nlm.nih.gov/pubmed/36502008 http://dx.doi.org/10.3390/s22239306 |
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author | Jiang, Hao Li, Lixia Zeng, Yuanyuan Fan, Jiajun Shen, Lijuan |
author_facet | Jiang, Hao Li, Lixia Zeng, Yuanyuan Fan, Jiajun Shen, Lijuan |
author_sort | Jiang, Hao |
collection | PubMed |
description | Hyperbolic embedding can effectively preserve the property of complex networks. Though some state-of-the-art hyperbolic node embedding approaches are proposed, most of them are still not well suited for the dynamic evolution process of temporal complex networks. The complexities of the adaptability and embedding update to the scale of complex networks with moderate variation are still challenging problems. To tackle the challenges, we propose hyperbolic embedding schemes for the temporal complex network within two dynamic evolution processes. First, we propose a low-complexity hyperbolic embedding scheme by using matrix perturbation, which is well-suitable for medium-scale complex networks with evolving temporal characteristics. Next, we construct the geometric initialization by merging nodes within the hyperbolic circular domain. To realize fast initialization for a large-scale network, an R tree is used to search the nodes to narrow down the search range. Our evaluations are implemented for both synthetic networks and realistic networks within different downstream applications. The results show that our hyperbolic embedding schemes have low complexity and are adaptable to networks with different scales for different downstream tasks. |
format | Online Article Text |
id | pubmed-9736245 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97362452022-12-11 Low-Complexity Hyperbolic Embedding Schemes for Temporal Complex Networks Jiang, Hao Li, Lixia Zeng, Yuanyuan Fan, Jiajun Shen, Lijuan Sensors (Basel) Article Hyperbolic embedding can effectively preserve the property of complex networks. Though some state-of-the-art hyperbolic node embedding approaches are proposed, most of them are still not well suited for the dynamic evolution process of temporal complex networks. The complexities of the adaptability and embedding update to the scale of complex networks with moderate variation are still challenging problems. To tackle the challenges, we propose hyperbolic embedding schemes for the temporal complex network within two dynamic evolution processes. First, we propose a low-complexity hyperbolic embedding scheme by using matrix perturbation, which is well-suitable for medium-scale complex networks with evolving temporal characteristics. Next, we construct the geometric initialization by merging nodes within the hyperbolic circular domain. To realize fast initialization for a large-scale network, an R tree is used to search the nodes to narrow down the search range. Our evaluations are implemented for both synthetic networks and realistic networks within different downstream applications. The results show that our hyperbolic embedding schemes have low complexity and are adaptable to networks with different scales for different downstream tasks. MDPI 2022-11-29 /pmc/articles/PMC9736245/ /pubmed/36502008 http://dx.doi.org/10.3390/s22239306 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Jiang, Hao Li, Lixia Zeng, Yuanyuan Fan, Jiajun Shen, Lijuan Low-Complexity Hyperbolic Embedding Schemes for Temporal Complex Networks |
title | Low-Complexity Hyperbolic Embedding Schemes for Temporal Complex Networks |
title_full | Low-Complexity Hyperbolic Embedding Schemes for Temporal Complex Networks |
title_fullStr | Low-Complexity Hyperbolic Embedding Schemes for Temporal Complex Networks |
title_full_unstemmed | Low-Complexity Hyperbolic Embedding Schemes for Temporal Complex Networks |
title_short | Low-Complexity Hyperbolic Embedding Schemes for Temporal Complex Networks |
title_sort | low-complexity hyperbolic embedding schemes for temporal complex networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9736245/ https://www.ncbi.nlm.nih.gov/pubmed/36502008 http://dx.doi.org/10.3390/s22239306 |
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