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A Semantic-Enhancement-Based Social Network User-Alignment Algorithm
User alignment can associate multiple social network accounts of the same user. It has important research implications. However, the same user has various behaviors and friends across different social networks. This will affect the accuracy of user alignment. In this paper, we aim to improve the acc...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858570/ https://www.ncbi.nlm.nih.gov/pubmed/36673313 http://dx.doi.org/10.3390/e25010172 |
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author | Huang, Yuanhao Zhao, Pengcheng Zhang, Qi Xing, Ling Wu, Honghai Ma, Huahong |
author_facet | Huang, Yuanhao Zhao, Pengcheng Zhang, Qi Xing, Ling Wu, Honghai Ma, Huahong |
author_sort | Huang, Yuanhao |
collection | PubMed |
description | User alignment can associate multiple social network accounts of the same user. It has important research implications. However, the same user has various behaviors and friends across different social networks. This will affect the accuracy of user alignment. In this paper, we aim to improve the accuracy of user alignment by reducing the semantic gap between the same user in different social networks. Therefore, we propose a semantically enhanced social network user alignment algorithm (SENUA). The algorithm performs user alignment based on user attributes, user-generated contents (UGCs), and user check-ins. The interference of local semantic noise can be reduced by mining the user’s semantic features for these three factors. In addition, we improve the algorithm’s adaptability to noise by multi-view graph-data augmentation. Too much similarity of non-aligned users can have a large negative impact on the user-alignment effect. Therefore, we optimize the embedding vectors based on multi-headed graph attention networks and multi-view contrastive learning. This can enhance the similar semantic features of the aligned users. Experimental results show that SENUA has an average improvement of 6.27% over the baseline method at hit-precision30. This shows that semantic enhancement can effectively improve user alignment. |
format | Online Article Text |
id | pubmed-9858570 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-98585702023-01-21 A Semantic-Enhancement-Based Social Network User-Alignment Algorithm Huang, Yuanhao Zhao, Pengcheng Zhang, Qi Xing, Ling Wu, Honghai Ma, Huahong Entropy (Basel) Article User alignment can associate multiple social network accounts of the same user. It has important research implications. However, the same user has various behaviors and friends across different social networks. This will affect the accuracy of user alignment. In this paper, we aim to improve the accuracy of user alignment by reducing the semantic gap between the same user in different social networks. Therefore, we propose a semantically enhanced social network user alignment algorithm (SENUA). The algorithm performs user alignment based on user attributes, user-generated contents (UGCs), and user check-ins. The interference of local semantic noise can be reduced by mining the user’s semantic features for these three factors. In addition, we improve the algorithm’s adaptability to noise by multi-view graph-data augmentation. Too much similarity of non-aligned users can have a large negative impact on the user-alignment effect. Therefore, we optimize the embedding vectors based on multi-headed graph attention networks and multi-view contrastive learning. This can enhance the similar semantic features of the aligned users. Experimental results show that SENUA has an average improvement of 6.27% over the baseline method at hit-precision30. This shows that semantic enhancement can effectively improve user alignment. MDPI 2023-01-15 /pmc/articles/PMC9858570/ /pubmed/36673313 http://dx.doi.org/10.3390/e25010172 Text en © 2023 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 Huang, Yuanhao Zhao, Pengcheng Zhang, Qi Xing, Ling Wu, Honghai Ma, Huahong A Semantic-Enhancement-Based Social Network User-Alignment Algorithm |
title | A Semantic-Enhancement-Based Social Network User-Alignment Algorithm |
title_full | A Semantic-Enhancement-Based Social Network User-Alignment Algorithm |
title_fullStr | A Semantic-Enhancement-Based Social Network User-Alignment Algorithm |
title_full_unstemmed | A Semantic-Enhancement-Based Social Network User-Alignment Algorithm |
title_short | A Semantic-Enhancement-Based Social Network User-Alignment Algorithm |
title_sort | semantic-enhancement-based social network user-alignment algorithm |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858570/ https://www.ncbi.nlm.nih.gov/pubmed/36673313 http://dx.doi.org/10.3390/e25010172 |
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