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首页> 外文期刊>Annals of the Institute of Statistical Mathematics >Directed hybrid random networks mixing preferential attachment with uniform attachment mechanisms
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Directed hybrid random networks mixing preferential attachment with uniform attachment mechanisms

机译：将优先附着与均匀附着机制混合的有向混合随机网络

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摘要

Motivated by the complexity of network data, we propose a directed hybrid random network that mixes preferential attachment (PA) rules with uniform attachment rules. When a new edge is created, with probability p is an element of (0, 1), it follows the PA rule. Otherwise, this new edge is added between two uniformly chosen nodes. Such mixture makes the in- and out-degrees of a fixed node grow at a slower rate, compared to the pure PA case, thus leading to lighter distributional tails. For estimation and inference, we develop two numerical methods which are applied to both synthetic and real network data. We see that with extra flexibility given by the parameter p, the hybrid random network provides a better fit to real-world scenarios, where lighter tails from in- and out-degrees are observed.
机译：受网络数据复杂性的驱使，我们提出了一种将优先依附（PA）规则与统一依附规则混合在一起的有向混合随机网络。创建新边时，概率 p 是 （0， 1） 的元素，它遵循 PA 规则。否则，将在两个统一选择的节点之间添加此新边。与纯PA情况相比，这种混合物使固定节点的入度和出度以较慢的速度增长，从而导致更轻的分布尾部。在估计和推理方面，我们开发了两种数值方法，分别应用于合成和真实网络数据。我们看到，由于参数 p 提供了额外的灵活性，混合随机网络提供了更好的拟合，在现实世界场景中，观察到来自内度和外度的较轻尾巴。

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