Link prediction of heterogeneous complex networks based on an improved embedding learning algorithm.
Link prediction in heterogeneous networks is an active research topic in the field of complex network science. Recognizing the limitations of existing methods, which often overlook the varying contributions of different local structures within these networks, this study introduces a novel algorithm...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0315507 |
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| _version_ | 1841533175715594240 |
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| author | Lang Chai Rui Huang |
| author_facet | Lang Chai Rui Huang |
| author_sort | Lang Chai |
| collection | DOAJ |
| description | Link prediction in heterogeneous networks is an active research topic in the field of complex network science. Recognizing the limitations of existing methods, which often overlook the varying contributions of different local structures within these networks, this study introduces a novel algorithm named SW-Metapath2vec. This algorithm enhances the embedding learning process by assigning weights to meta-path traces generated through random walks and translates the potential connections between nodes into the cosine similarity of embedded vectors. The study was conducted using multiple real-world and synthetic datasets to validate the proposed algorithm's performance. The results indicate that SW-Metapath2vec significantly outperforms benchmark algorithms. Notably, the algorithm maintains high predictive performance even when a substantial proportion of network nodes are removed, demonstrating its resilience and potential for practical application in analyzing large-scale heterogeneous networks. These findings contribute to the advancement of link prediction techniques and offer valuable insights and tools for related research areas. |
| format | Article |
| id | doaj-art-8d3ac942db034d2a8beadbc95700098d |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-8d3ac942db034d2a8beadbc95700098d2025-01-17T05:31:39ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031550710.1371/journal.pone.0315507Link prediction of heterogeneous complex networks based on an improved embedding learning algorithm.Lang ChaiRui HuangLink prediction in heterogeneous networks is an active research topic in the field of complex network science. Recognizing the limitations of existing methods, which often overlook the varying contributions of different local structures within these networks, this study introduces a novel algorithm named SW-Metapath2vec. This algorithm enhances the embedding learning process by assigning weights to meta-path traces generated through random walks and translates the potential connections between nodes into the cosine similarity of embedded vectors. The study was conducted using multiple real-world and synthetic datasets to validate the proposed algorithm's performance. The results indicate that SW-Metapath2vec significantly outperforms benchmark algorithms. Notably, the algorithm maintains high predictive performance even when a substantial proportion of network nodes are removed, demonstrating its resilience and potential for practical application in analyzing large-scale heterogeneous networks. These findings contribute to the advancement of link prediction techniques and offer valuable insights and tools for related research areas.https://doi.org/10.1371/journal.pone.0315507 |
| spellingShingle | Lang Chai Rui Huang Link prediction of heterogeneous complex networks based on an improved embedding learning algorithm. PLoS ONE |
| title | Link prediction of heterogeneous complex networks based on an improved embedding learning algorithm. |
| title_full | Link prediction of heterogeneous complex networks based on an improved embedding learning algorithm. |
| title_fullStr | Link prediction of heterogeneous complex networks based on an improved embedding learning algorithm. |
| title_full_unstemmed | Link prediction of heterogeneous complex networks based on an improved embedding learning algorithm. |
| title_short | Link prediction of heterogeneous complex networks based on an improved embedding learning algorithm. |
| title_sort | link prediction of heterogeneous complex networks based on an improved embedding learning algorithm |
| url | https://doi.org/10.1371/journal.pone.0315507 |
| work_keys_str_mv | AT langchai linkpredictionofheterogeneouscomplexnetworksbasedonanimprovedembeddinglearningalgorithm AT ruihuang linkpredictionofheterogeneouscomplexnetworksbasedonanimprovedembeddinglearningalgorithm |