论文标题
在共同发展网络中非线性相互作用和噪声中复杂结构的出现
Emergence of complex structures from nonlinear interactions and noise in coevolving networks
论文作者
论文摘要
我们研究了相互作用和噪声对协同进化动力学的关节作用。我们选择共同发展的选民模型作为此问题的原型框架。通过数值模拟和分析近似值,我们发现了三个主要阶段,它们的绝对磁化和最大成分的大小有所不同:共识阶段,共存阶段和动态碎片阶段。更详细的分析揭示了这些阶段的内在差异,使我们可以进一步划分其中两个。在共识阶段,我们可以区分弱或交替的共识(在两个相反的共识状态之间切换)和强大的共识,其中系统保持在相同的状态,以全面实现随机动力学。另外,弱和强共识阶段的规模与系统大小不同。超线性相互作用的强烈共识阶段,这是在热力学极限中生存的唯一共识阶段。在共存阶段,我们区分了一个完全混合的阶段(两个状态在网络中充分混合)和一个结构化共存阶段,其中连接不同状态中的节点(主动链接)的链接数量大大下降,这是由于两个相反状态连接的两个均质社区的形成。结构化共存阶段是社区结构出现的一个例子,而不是仅仅是拓扑动态,而是协同进化。我们的数值观察得到了分析描述的支持,使用对近似方法和对共存和动态碎片阶段之间过渡的临时计算。我们的工作表明了简单的交互规则,包括非线性,噪声和协同进化的联合效应导致与社会系统描述相关的复杂结构。
We study the joint effect of the non-linearity of interactions and noise on coevolutionary dynamics. We choose the coevolving voter model as a prototype framework for this problem. By numerical simulations and analytical approximations we find three main phases that differ in the absolute magnetization and the size of the largest component: a consensus phase, a coexistence phase, and a dynamical fragmentation phase. More detailed analysis reveals inner differences in these phases, allowing us to divide two of them further. In the consensus phase we can distinguish between a weak or alternating consensus (switching between two opposite consensus states), and a strong consensus, in which the system remains in the same state for the whole realization of the stochastic dynamics. Additionally, weak and strong consensus phases scale differently with the system size. The strong consensus phase exists for superlinear interactions and it is the only consensus phase that survives in the thermodynamic limit. In the coexistence phase we distinguish a fully-mixing phase (both states well mixed in the network) and a structured coexistence phase, where the number of links connecting nodes in different states (active links) drops significantly due to the formation of two homogeneous communities of opposite states connected by a few links. The structured coexistence phase is an example of emergence of community structure from not exclusively topological dynamics, but coevolution. Our numerical observations are supported by an analytical description using a pair approximation approach and an ad-hoc calculation for the transition between the coexistence and dynamical fragmentation phases. Our work shows how simple interaction rules including the joint effect of non-linearity, noise, and coevolution lead to complex structures relevant in the description of social systems.