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Hypergraph reconstruction from uncertain pairwise observations
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OA
AI
DOI：10.1038/s41598-023-48081-w.png)
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摘要
En 中文
The network reconstruction task aims to estimate a complex system's structure from various data sources such as time series, snapshots, or interaction counts. Recent work has examined this problem in networks whose relationships involve precisely two entities-the pairwise case. Here, using Bayesian inference, we investigate the general problem of reconstructing a network in which higher-order interactions are also present. We study a minimal example of this problem, focusing on the case of hypergraphs with interactions between pairs and triplets of vertices, measured imperfectly and indirectly. We derive a Metropolis-Hastings-within-Gibbs algorithm for this model to highlight the unique challenges that come with estimating higher-order models. We show that this approach tends to reconstruct empirical and synthetic networks more accurately than an equivalent graph model without higher-order interactions.
Keyword:
HIGHER-ORDER INTERACTIONS
NETWORKS
MODEL
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期刊
IF:
3.9
论文数:
27.9W
被引数:
83.5W
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