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摘要翻译：
研究了一种根据节点状态演化的网络协同进化选民模型。在一次更新中，相对状态节点之间的链路以$P$的概率重新布线，而以$1-P$的概率，其中一个节点采用其邻居的状态。平均场近似揭示了在一个临界值$P_c=\frac{\mu-2}{\mu-1}$处从活跃相到冻结相的吸收转变，该临界值仅依赖于网络的平均度$\mu$。对最终态的接近是由一个在临界点发散的时间标度表示的，它是$\tau\sim p_c-p^{-1}$。我们发现，激活和冻结阶段分别对应于一个连通和一个碎片网络。我们证明了有限规模系统中的过渡可以看作是等效随机游动轨迹在临界重布线速率$P_c$下的突然变化，强调了过渡背后的机制是网络和节点状态演化速率之间的竞争。
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英文标题：
《Generic Absorbing Transition in Coevolution Dynamics》
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作者：
F. Vazquez, V.M. Eguiluz and M. San Miguel
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最新提交年份：
2008
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分类信息：
一级分类：Physics 物理学
二级分类：Physics and Society 物理学与社会
分类描述：Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体（人类或其他）的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统（如能源网、运输网络）的物理和工程。
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一级分类：Physics 物理学
二级分类：Statistical Mechanics 统计力学
分类描述：Phase transitions, thermodynamics, field theory, non-equilibrium phenomena, renormalization group and scaling, integrable models, turbulence
相变，热力学，场论，非平衡现象，重整化群和标度，可积模型，湍流
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英文摘要：
We study a coevolution voter model on a network that evolves according to the state of the nodes. In a single update, a link between opposite-state nodes is rewired with probability $p$, while with probability $1-p$ one of the nodes takes its neighbor's state. A mean-field approximation reveals an absorbing transition from an active to a frozen phase at a critical value $p_c=\frac{\mu-2}{\mu-1}$ that only depends on the average degree $\mu$ of the network. The approach to the final state is characterized by a time scale that diverges at the critical point as $\tau \sim |p_c-p|^{-1}$. We find that the active and frozen phases correspond to a connected and a fragmented network respectively. We show that the transition in finite-size systems can be seen as the sudden change in the trajectory of an equivalent random walk at the critical rewiring rate $p_c$, highlighting the fact that the mechanism behind the transition is a competition between the rates at which the network and the state of the nodes evolve.
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PDF链接：
https://arxiv.org/pdf/710.491
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