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arXiv 2203.01014cond-mat.stat-mechcs.SIphysics.soc-ph
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累积合并渗流:网络中的长程渗流过程

Cumulative Merging Percolation: A long-range percolation process in networks

Lorenzo Cirigliano, Giulio Cimini, Romualdo Pastor-Satorras, Claudio Castellano

AI总结:

本文提出了长程渗流过程CMP的更一般表述,通过簇相互作用范围的函数形式揭示了不同机制竞争导致的交叉现象等更丰富的相变场景,并得到数值模拟验证。

摘要:

网络上的渗流是建模从级联失效到流行病传播等多种过程的常用框架。标准渗流假设短程相互作用,意味着节点只有在互为最近邻时才能合并成簇。累积合并渗流(CMP)是一种新的渗流过程,它假设长程相互作用,使得节点即使在拓扑上相距遥远也能合并成簇。因此,在CMP中,渗流簇与网络的拓扑连通分量并不重合。先前的工作表明,CMP的一种特定形式具有形成巨簇的特殊机制,并允许对诸如反复发作的流行病过程等不同的网络动力学进行建模。在这里,我们根据簇相互作用范围的函数形式开发了CMP的一个更一般的表述,展示了一个更丰富的相变场景,其中不同机制的竞争导致了交叉现象。我们的解析预测得到了数值模拟的证实。

英文摘要:

Percolation on networks is a common framework to model a wide range of processes, from cascading failures to epidemic spreading. Standard percolation assumes short-range interactions, implying that nodes can merge into clusters only if they are nearest-neighbors. Cumulative Merging Percolation (CMP) is an new percolation process that assumes long-range interactions, such that nodes can merge into clusters even if they are topologically distant. Hence in CMP percolation clusters do not coincide with the topological connected components of the network. Previous work has shown that a specific formulation of CMP features peculiar mechanisms for the formation of the giant cluster, and allows to model different network dynamics such as recurrent epidemic processes. Here we develop a more general formulation of CMP in terms of the functional form of the cluster interaction range, showing an even richer phase transition scenario with competition of different mechanisms resulting in crossover phenomena. Our analytic predictions are confirmed by numerical simulations.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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