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arXiv 2102.00224cond-mat.stat-mech
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爆炸逾渗转变中信息熵的标度行为

Scaling behavior of information entropy in explosive percolation transitions

Yejun Kang, Young Sul Cho

摘要:

爆炸逾渗转变是由于大团簇的生长受到抑制而在阈值处突然出现的巨团簇。在本文中,我们考虑的信息熵的集群大小分布,这是一个随机选择的集群的大小的概率分布。据报道,在爆炸逾渗模型中,信息熵在阈值处不会达到最大值,这一结果似乎与之前的其他结果相反,即团簇大小分布呈幂律行为,并且团簇大小多样性(不同团簇大小的数量)在阈值处最大。在这里,我们表明,这种现象是由于集群大小分布的缩放形式是不同的阈值以下和以上。我们还建立了信息熵的一阶和二阶导数在阈值附近的标度行为,以解释为什么一阶导数在阈值处具有负最小值,而二阶导数在阈值的左(右)极限处负(正)发散,正如之前的模拟预测的那样。

英文摘要:

An explosive percolation transition is the abrupt emergence of a giant cluster at a threshold caused by a suppression of the growth of large clusters. In this paper, we consider the information entropy of the cluster size distribution, which is the probability distribution for the size of a randomly chosen cluster. It has been reported that information entropy does not reach its maximum at the threshold in explosive percolation models, a result seemingly contrary to other previous results that the cluster size distribution shows power-law behavior and the cluster size diversity (number of distinct cluster sizes) is maximum at the threshold. Here, we show that this phenomenon is due to that the scaling form of the cluster size distribution is given differently below and above the threshold. We also establish the scaling behaviors of the first and second derivatives of the information entropy near the threshold to explain why the first derivative has a negative minimum at the threshold and the second derivative diverges negatively (positively) at the left (right) limit of the threshold, as predicted through previous simulation.

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