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Normal Approximation for Subgraph Count in Random Hypergraphs
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DOI：10.1002/jgt.70066.png)
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Abstract
En 中文
A nonuniform and inhomogeneous random hypergraph model is considered, which is a straightforward extension of the celebrated binomial random graph model G(n, p). We establish necessary and sufficient conditions for the small hypergraph count to be asymptotically normal, and complement them with convergence rates in both the Wasserstein and Kolmogorov distances. Next we narrow our attention to the homogeneous model and relate the obtained results to the fourth moment phenomenon. Additionally, a short proof of the necessity of the aforementioned conditions is presented, which seems to be absent in the literature, even in the context of the model G(n, p).
Keywords:
asymptotic normality
Kolmogorov distance
random hypergraph
subgraph count
Wasserstein distance
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J
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1
Papers:
65
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