随机网络上的有限模仿传染：混沌、普适性和不可预测性
Limited Imitation Contagion on Random Networks: Chaos, Universality, and Unpredictability
摘要：
我们研究了一个家庭的二进制状态，社会启发的传染模型，其中包括模仿的厌恶完全一致的限制。我们发现丰富的行为在我们的模型中，无论是在动态和固定的随机网络上的概率或确定性的个人响应函数。特别是，我们发现人口的感染分数的限制行为的显着变化，从稳态到混沌。我们发现，倍周期的出现，因为我们增加的平均节点度，这一众所周知的路线的普遍性类混沌依赖于随机网络的相互作用结构，而不是个别节点的微观行为。我们发现，增加系统的固定性往往会稳定的感染分数，但不相交的，多个平衡是可能的，这完全取决于最初感染节点的选择。
英文摘要：
We study a family of binary state, socially-inspired contagion models which incorporate imitation limited by an aversion to complete conformity. We uncover rich behavior in our models whether operating with either probabilistic or deterministic individual response functions on both dynamic and fixed random networks. In particular, we find significant variation in the limiting behavior of a population's infected fraction, ranging from steady-state to chaotic. We show that period doubling arises as we increase the average node degree, and that the universality class of this well known route to chaos depends on the interaction structure of random networks rather than the microscopic behavior of individual nodes. We find that increasing the fixedness of the system tends to stabilize the infected fraction, yet disjoint, multiple equilibria are possible depending solely on the choice of the initially infected node.
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