In network models for real-world domains, often network adaptation has to be addressed by incorporating certain network adaptation principles. In some cases, also higher order adaptation occurs: the adaptation principles themselves also change over time. To model such multilevel adaptation processes, it is useful to have some generic architecture. Such an architecture should describe and distinguish the dynamics within the network (base level), but also the dynamics of the network itself by certain adaptation principles (first-order adaptation level), and also the adaptation of these adaptation principles (second-order adaptation level), and may be still more levels of higher order adaptation. This paper introduces a multilevel network architecture for this, based on the notion network reification. Reification of a network occurs when a base network is extended by adding explicit states representing the characteristics of the structure of the base network. It will be shown how this construction can be used to explicitly represent network adaptation principles within a network. When the reified network is itself also reified, also second-order adaptation principles can be explicitly represented. The multilevel network reification construction introduced here is illustrated for an adaptive adaptation principle from social science for bonding based on homophily and one for metaplasticity in Cognitive Neuroscience.

中文翻译：

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通过多级网络具体化对网络的高阶适应性建模

在现实世界领域的网络模型中，通常必须通过结合某些网络适应原则来解决网络适应问题。在某些情况下，还会出现更高阶的适应：适应原则本身也会随着时间而变化。为了对这种多级适应过程进行建模，拥有一些通用架构是很有用的。这样的架构既要描述和区分网络内部的动态（基础层），又要描述和区分网络本身的动态，通过一定的适配原则（一阶适配层），以及这些适配原则的适配（二阶适配）级别），并且可能是更高阶适应的更多级别。本文介绍了一种基于概念网络具体化的多级网络架构。当通过添加表示基础网络结构特征的显式状态来扩展基础网络时，就会发生网络的具体化。将展示如何使用这种结构来明确表示网络内的网络自适应原则。当物化网络本身也被物化时，也可以明确表示二阶适应原则。这里介绍的多级网络具体化构造用于说明社会科学中基于同质性的结合的自适应适应原则和认知神经科学中的元可塑性。当物化网络本身也被物化时，也可以明确表示二阶适应原则。这里介绍的多级网络具体化构造用于说明社会科学中基于同质性的结合的自适应适应原则和认知神经科学中的元可塑性。当物化网络本身也被物化时，也可以明确表示二阶适应原则。这里介绍的多级网络具体化构造用于说明社会科学中基于同质性的结合的自适应适应原则和认知神经科学中的元可塑性。