基于图的级联
Cascades on clique-based graphs
摘要：
我们提出了一种分析方法，以确定预期的级联大小在广泛的动态模型的高度聚类的随机图类Gleeson [J. P. Gleeson，物理。Rev. E 80，036107（2009）]。还导出了全局级联存在的条件。这种方法的应用包括渗流分析和Watts模型。我们展示了我们的技术如何可以用来研究内群体偏见的级联效应对社交网络。
英文摘要：
We present an analytical approach to determining the expected cascade size in a broad range of dynamical models on the class of highly-clustered random graphs introduced by Gleeson [J. P. Gleeson, Phys. Rev. E 80, 036107 (2009)]. A condition for the existence of global cascades is also derived. Applications of this approach include analyses of percolation, and Watts's model. We show how our techniques can be used to study the effects of in-group bias in cascades on social networks.
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