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Cascades and Myopic Routing in Nonhomogeneous Kleinberg's Small World Model

机译:非均匀Kleinberg小世界模型中的级联和近视路径

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Kleinberg's small world model [20] simulates social networks with both strong and weak ties. In his original paper, Kleinberg showed how the distribution of weak-ties, parameterized by γ, influences the efficacy of myopic routing on the network. Recent work on social influence by k-complex contagion models discovered that the distribution of weak-ties also impacts the spreading rate in a crucial manner on Kleinberg's small world model [15]. In both cases the parameter of γ = 2 proves special: when γ is anything but 2 the properties no longer hold. In this work, we propose a natural generalization of Kleinberg's small world model to allow node heterogeneity: instead of a single global parameter γ, each node has a personalized parameter γ chosen independently from α distribution D. In contrast to the original model, we show that this model enables myopic routing and k-complex contagions on a large range of the parameter space, improving the robustness of the model. Moreover, we show that our generalization is supported by real-world data. Analysis of four different social networks shows that the nodes do not show homogeneity in terms of the variance of the lengths of edges incident to the same node.
机译:克莱因伯格的小世界模型[20]模拟了紧密联系和紧密联系的社交网络。 Kleinberg在他的原始论文中显示了以γ为参数的弱连接的分布如何影响网络中近视路由的效率。最近通过k复杂传染模型对社会影响进行的研究发现,弱势关系的分布也对克莱因伯格的小世界模型产生了至关重要的影响[15]。在这两种情况下,γ= 2的参数都证明是特殊的:当γ不为2时,属性不再成立。在这项工作中,我们提出了Kleinberg小世界模型的自然概括,以允许节点异质性:除了单个全局参数γ之外,每个节点还具有独立于α分布D选择的个性化参数γ。与原始模型相反,我们展示了该模型可以在很大范围的参数空间上实现近视路由和k复数传染,从而提高了模型的鲁棒性。此外,我们证明了我们的概括得到了现实世界数据的支持。对四个不同社交网络的分析表明,就入射到同一节点的边的长度的方差而言,节点没有表现出同质性。

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