Reconstructing networks from simple and complex contagions
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916426969776128 |
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| author | Landry, Nicholas W. Thompson, William Hébert-Dufresne, Laurent Young, Jean-Gabriel |
| author_facet | Landry, Nicholas W. Thompson, William Hébert-Dufresne, Laurent Young, Jean-Gabriel |
| contents | Network scientists often use complex dynamic processes to describe network contagions, but tools for fitting contagion models typically assume simple dynamics. Here, we address this gap by developing a nonparametric method to reconstruct a network and dynamics from a series of node states, using a model that breaks the dichotomy between simple pairwise and complex neighborhood-based contagions. We then show that a network is more easily reconstructed when observed through the lens of complex contagions if it is dense or the dynamic saturates, and that simple contagions are better otherwise. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_00129 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Reconstructing networks from simple and complex contagions Landry, Nicholas W. Thompson, William Hébert-Dufresne, Laurent Young, Jean-Gabriel Social and Information Networks Populations and Evolution Machine Learning Network scientists often use complex dynamic processes to describe network contagions, but tools for fitting contagion models typically assume simple dynamics. Here, we address this gap by developing a nonparametric method to reconstruct a network and dynamics from a series of node states, using a model that breaks the dichotomy between simple pairwise and complex neighborhood-based contagions. We then show that a network is more easily reconstructed when observed through the lens of complex contagions if it is dense or the dynamic saturates, and that simple contagions are better otherwise. |
| title | Reconstructing networks from simple and complex contagions |
| topic | Social and Information Networks Populations and Evolution Machine Learning |
| url | https://arxiv.org/abs/2405.00129 |