标题： 度相关性在网络中增强级联的增长 显示英文标题

标题： Degree Correlations Amplify the Growth of Cascades in Networks

摘要： 网络促进了级联的传播，使得局部扰动可以通过节点及其邻居之间的相互作用渗透。 我们研究了网络结构如何影响级联传播的动力学。 通过在生成函数框架内考虑网络的联合度分布，我们可以量化度相关性如何影响全局级联的开始以及特定度类节点触发大规模级联的可能性。 然而，并非所有的度相关性在传播过程中都同样重要。 我们引入了一种新的度同配性度量，该度量考虑了与级联传播相关的节点之间的相关性。 我们证明了定义全局级联开始的关键点与这种新的同配性度量之间存在单调关系。 此外，我们证明了选择种子节点以引发最大级联的选择受到度相关性的影响很大。 与传统观点相反，当度同配性为正时，低度节点更有可能产生最大的级联。 我们的研究表明，可能通过操纵网络的高阶结构来定制传播过程。 显示英文摘要

摘要： Networks facilitate the spread of cascades, allowing a local perturbation to percolate via interactions between nodes and their neighbors. We investigate how network structure affects the dynamics of a spreading cascade. By accounting for the joint degree distribution of a network within a generating function framework, we can quantify how degree correlations affect both the onset of global cascades and the propensity of nodes of specific degree class to trigger large cascades. However, not all degree correlations are equally important in a spreading process. We introduce a new measure of degree assortativity that accounts for correlations among nodes relevant to a spreading cascade. We show that the critical point defining the onset of global cascades has a monotone relationship to this new assortativity measure. In addition, we show that the choice of nodes to seed the largest cascades is strongly affected by degree correlations. Contrary to traditional wisdom, when degree assortativity is positive, low degree nodes are more likely to generate largest cascades. Our work suggests that it may be possible to tailor spreading processes by manipulating the higher-order structure of networks.