Link prediction using extended neighborhood based local random walk in multilayer social networks
One of these challenges in the analysis of social networks is the problem of link prediction. The purpose of this problem is to find links that have not yet been observed, but may exist in the future. There are many solutions for link prediction on monoplex networks. However, many real social networ...
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2024-02-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S131915782400020X |
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| author | Xueping Ren |
| author_facet | Xueping Ren |
| author_sort | Xueping Ren |
| collection | DOAJ |
| description | One of these challenges in the analysis of social networks is the problem of link prediction. The purpose of this problem is to find links that have not yet been observed, but may exist in the future. There are many solutions for link prediction on monoplex networks. However, many real social networks model communication in multiple layers, which are known as multilayer social networks. A solution for multilayer networks involves taking into account the information of all layers to make predictions for a target layer. Among the existing solutions, local random walk has been confirmed as an efficient technique for link prediction in monoplex networks, but this technique is inefficient for link prediction in multilayer networks due to computational complexity. In order to address this issue, in this paper we propose Extended Neighborhood based Local Random Walk (ENLRW) for link prediction in multilayer networks. ENLRW is an extended version of the classical local random walk technique in which the nearest neighbors are considered based on the extended neighborhood concept. ENLRW calculates the similarity between vertices by integrating several different metrics through reliable paths that include intra-layer and inter-layer information. Besides, ENLRW considers vertex influence as a similarity metric to provide an effective reliable biased random walk. The results of the simulations show that the use of different inter-layer and intra-layer information as well as the local random walk configuration with extended neighborhood provides a trade-off between precision and complexity. Specifically, ENLRW improves the average precision by 3.1% compared to the best available state-of-the-art method. |
| format | Article |
| id | doaj-art-38dde58f588b4bafa8a4743f6e1a77ad |
| institution | Kabale University |
| issn | 1319-1578 |
| language | English |
| publishDate | 2024-02-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-38dde58f588b4bafa8a4743f6e1a77ad2025-08-20T03:55:11ZengSpringerJournal of King Saud University: Computer and Information Sciences1319-15782024-02-0136210193110.1016/j.jksuci.2024.101931Link prediction using extended neighborhood based local random walk in multilayer social networksXueping Ren0Information Engineering College, Hangzhou Dianzi University, Hangzhou, Zhejiang 311305, ChinaOne of these challenges in the analysis of social networks is the problem of link prediction. The purpose of this problem is to find links that have not yet been observed, but may exist in the future. There are many solutions for link prediction on monoplex networks. However, many real social networks model communication in multiple layers, which are known as multilayer social networks. A solution for multilayer networks involves taking into account the information of all layers to make predictions for a target layer. Among the existing solutions, local random walk has been confirmed as an efficient technique for link prediction in monoplex networks, but this technique is inefficient for link prediction in multilayer networks due to computational complexity. In order to address this issue, in this paper we propose Extended Neighborhood based Local Random Walk (ENLRW) for link prediction in multilayer networks. ENLRW is an extended version of the classical local random walk technique in which the nearest neighbors are considered based on the extended neighborhood concept. ENLRW calculates the similarity between vertices by integrating several different metrics through reliable paths that include intra-layer and inter-layer information. Besides, ENLRW considers vertex influence as a similarity metric to provide an effective reliable biased random walk. The results of the simulations show that the use of different inter-layer and intra-layer information as well as the local random walk configuration with extended neighborhood provides a trade-off between precision and complexity. Specifically, ENLRW improves the average precision by 3.1% compared to the best available state-of-the-art method.http://www.sciencedirect.com/science/article/pii/S131915782400020XMultilayer social networksLink predictionLocal random walkVertex influenceExtended neighborhood |
| spellingShingle | Xueping Ren Link prediction using extended neighborhood based local random walk in multilayer social networks Journal of King Saud University: Computer and Information Sciences Multilayer social networks Link prediction Local random walk Vertex influence Extended neighborhood |
| title | Link prediction using extended neighborhood based local random walk in multilayer social networks |
| title_full | Link prediction using extended neighborhood based local random walk in multilayer social networks |
| title_fullStr | Link prediction using extended neighborhood based local random walk in multilayer social networks |
| title_full_unstemmed | Link prediction using extended neighborhood based local random walk in multilayer social networks |
| title_short | Link prediction using extended neighborhood based local random walk in multilayer social networks |
| title_sort | link prediction using extended neighborhood based local random walk in multilayer social networks |
| topic | Multilayer social networks Link prediction Local random walk Vertex influence Extended neighborhood |
| url | http://www.sciencedirect.com/science/article/pii/S131915782400020X |
| work_keys_str_mv | AT xuepingren linkpredictionusingextendedneighborhoodbasedlocalrandomwalkinmultilayersocialnetworks |