Hypergraph reconstruction from network data
Higher-order interactions intervene in a large variety of networked phenomena, from shared interests known to influence the creation of social ties, to co-location shaping networks embedded in space, like power grids. This work introduces a Bayesian framework to infer higher-order interactions hidde...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2021-06-01
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| Series: | Communications Physics |
| Online Access: | https://doi.org/10.1038/s42005-021-00637-w |
| Summary: | Higher-order interactions intervene in a large variety of networked phenomena, from shared interests known to influence the creation of social ties, to co-location shaping networks embedded in space, like power grids. This work introduces a Bayesian framework to infer higher-order interactions hidden in network data. |
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| ISSN: | 2399-3650 |