SDW2vec: learning structural representations of nodes in weighted networks
Abstract Recent advances in machine learning have prompted researchers to integrate complex network structures into computational frameworks to improve inferential capabilities. Node embedding has become a promising technique in this area. However, challenges persist in accurately representing the s...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-07-01
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| Series: | Applied Network Science |
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| Online Access: | https://doi.org/10.1007/s41109-025-00722-x |
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| author | Shu Liu Masaki Chujyo Fujio Toriumi |
| author_facet | Shu Liu Masaki Chujyo Fujio Toriumi |
| author_sort | Shu Liu |
| collection | DOAJ |
| description | Abstract Recent advances in machine learning have prompted researchers to integrate complex network structures into computational frameworks to improve inferential capabilities. Node embedding has become a promising technique in this area. However, challenges persist in accurately representing the structural characteristics of nodes in weighted networks. In this study, we propose SDW2vec, which learns the embeddings of nodes in weighted networks while preserving structural properties. Our proposed methodology addresses these challenges through a multi-scale comparison of link weights among adjacent nodes up to a predefined hop count. This approach facilitates the calculation of distances between nodes’ structural configurations across multiple scales. We subsequently construct weighted multi-layer graphs based on these distance measurements, apply random walks to generate node sequences, and learn the embedding representations using the Skip-gram model. The efficacy of our methodology is validated through both the interpretability of embedding representations in controlled network environments and the structural reproducibility demonstrated in real-world networks. |
| format | Article |
| id | doaj-art-c9b2b7ed026f43c2a01d7e586b9bfaf1 |
| institution | Kabale University |
| issn | 2364-8228 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Applied Network Science |
| spelling | doaj-art-c9b2b7ed026f43c2a01d7e586b9bfaf12025-08-20T03:45:57ZengSpringerOpenApplied Network Science2364-82282025-07-0110111510.1007/s41109-025-00722-xSDW2vec: learning structural representations of nodes in weighted networksShu Liu0Masaki Chujyo1Fujio Toriumi2Department of Systems Innovation, Faculty of Engineering, The University of TokyoDepartment of Systems Innovation, Faculty of Engineering, The University of TokyoDepartment of Systems Innovation, Faculty of Engineering, The University of TokyoAbstract Recent advances in machine learning have prompted researchers to integrate complex network structures into computational frameworks to improve inferential capabilities. Node embedding has become a promising technique in this area. However, challenges persist in accurately representing the structural characteristics of nodes in weighted networks. In this study, we propose SDW2vec, which learns the embeddings of nodes in weighted networks while preserving structural properties. Our proposed methodology addresses these challenges through a multi-scale comparison of link weights among adjacent nodes up to a predefined hop count. This approach facilitates the calculation of distances between nodes’ structural configurations across multiple scales. We subsequently construct weighted multi-layer graphs based on these distance measurements, apply random walks to generate node sequences, and learn the embedding representations using the Skip-gram model. The efficacy of our methodology is validated through both the interpretability of embedding representations in controlled network environments and the structural reproducibility demonstrated in real-world networks.https://doi.org/10.1007/s41109-025-00722-xRepresentation learningNode embeddingStructural embeddingWeighted networksSigned directed networks |
| spellingShingle | Shu Liu Masaki Chujyo Fujio Toriumi SDW2vec: learning structural representations of nodes in weighted networks Applied Network Science Representation learning Node embedding Structural embedding Weighted networks Signed directed networks |
| title | SDW2vec: learning structural representations of nodes in weighted networks |
| title_full | SDW2vec: learning structural representations of nodes in weighted networks |
| title_fullStr | SDW2vec: learning structural representations of nodes in weighted networks |
| title_full_unstemmed | SDW2vec: learning structural representations of nodes in weighted networks |
| title_short | SDW2vec: learning structural representations of nodes in weighted networks |
| title_sort | sdw2vec learning structural representations of nodes in weighted networks |
| topic | Representation learning Node embedding Structural embedding Weighted networks Signed directed networks |
| url | https://doi.org/10.1007/s41109-025-00722-x |
| work_keys_str_mv | AT shuliu sdw2veclearningstructuralrepresentationsofnodesinweightednetworks AT masakichujyo sdw2veclearningstructuralrepresentationsofnodesinweightednetworks AT fujiotoriumi sdw2veclearningstructuralrepresentationsofnodesinweightednetworks |