Hackett Adam, Gleeson James P
Hamilton Institute, National University of Ireland, Maynooth, Co. Kildare, Ireland.
Phys Rev E Stat Nonlin Soft Matter Phys. 2013 Jun;87(6):062801. doi: 10.1103/PhysRevE.87.062801. Epub 2013 Jun 3.
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.
我们提出了一种分析方法，用于确定在Gleeson [J. P. Gleeson, 《物理评论E》80, 036107 (2009)] 引入的高度聚类随机图类上的广泛动力学模型中的预期级联规模。还推导了全局级联存在的条件。这种方法的应用包括渗流分析和瓦茨模型。我们展示了如何使用我们的技术来研究级联中的内群体偏差对社交网络的影响。
