随机网络中耦合动力学的拓扑转变
Topological transition in a coupled dynamic in random networks
摘要：
在这项工作中，我们研究了Saeedian等人提出的模型中相关网络的拓扑转变（Scientific Reports 2019 9 9：9726），该模型考虑了节点和链路状态的耦合动力学。我们的目标是更好地理解观察到的两个阶段，因此我们使用另一种网络结构（所谓的随机几何图- RGG）以及从网络科学借鉴的其他度量。我们观察到两个相关网络的拓扑转变，这是完整网络的子图。由于链接有两种可能的状态（友好和非友好），我们将每个关联网络定义为仅由一种类型的链接组成。（非）友好关联网络仅具有（非）友好链接。这种拓扑转变是从系统的两个阶段（吸收和活性）之间的每个相关网络的域分布观察到的。我们还证明了另一个来自网络科学的度量标准，称为模块性（或排斥系数），也可以用作序参量，给出与开创性工作中原始序参量相同的相图。在吸收阶段，每个相关网络的模块度的绝对值达到最大值，而在活动阶段，它们下降到最小值。
英文摘要：
In this work, we study the topological transition on the associated networks in a model proposed by Saeedian et al. (Scientific Reports 2019 9:9726), which considers a coupled dynamics of node and link states. Our goal was to better understand the two observed phases, so we use another network structure (the so called random geometric graph - RGG) together with other metrics borrowed from network science. We observed a topological transition on the two associated networks, which are subgraphs of the full network. As the links have two possible states (friendly and non-friendly), we defined each associated network as composed of only one type of link. The (non) friendly associated network has (non) friendly links only. This topological transition was observed from the domain distribution of each associated network between the two phases of the system (absorbing and active). We also showed that another metric from network science called modularity (or assortative coefficient) can also be used as order parameter, giving the same phase diagram as the original order parameter from the seminal work. On the absorbing phase the absolute value of the modularity for each associated network reaches a maximum value, while on the active phase they fall to the minimum value.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
