Machine learning meets complex networks via coalescent embedding in the hyperbolic space
Mapping complex networks to underlying geometric spaces can help understand the structure of networked systems. Here the authors propose a class of machine learning algorithms for efficient embedding of large real networks to the hyperbolic space, with potential impact on big network data analysis.
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| Main Authors: | , , , , |
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| Format: | article |
| Language: | EN |
| Published: |
Nature Portfolio
2017
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| Subjects: | |
| Online Access: | https://doaj.org/article/d1158773a17e4a07802f5e3302700f6a |
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