时态网络的随机参考模型
Randomized reference models for temporal networks
摘要：
许多动态系统可以通过将它们表示为网络来成功地分析。经验测量的网络和动态过程，发生在这些情况下显示异构，非马尔可夫，和内在相关的拓扑结构和动态。这使得他们的分析特别具有挑战性。随机参考模型（RRM）已成为研究此类系统的通用工具箱。定义为随机网络，其给定特征被约束为与输入（经验）网络的特征相匹配，例如，它们可以用于识别经验网络的重要特征及其对网络中展开的动态过程的影响。RRM通常被实现为重新洗牌经验网络的过程，使其具有非常普遍的适用性。然而，大多数洗牌程序对网络功能的影响仍然知之甚少，使得它们的使用不平凡，容易被误解。在这里，我们提出了一个统一的框架，用于分类和理解微正则RRM（MRRM），以均匀的概率对网络进行采样。专注于时间网络，我们调查的MRRM在文献中发现的应用程序，我们使用这个框架来建立一个分类的MRRM，提出了一个规范的命名约定，分类，并推断其影响的一系列重要的网络功能。我们还表明，某些类别的MRRM可以应用于顺序组成，以产生新的MRRM从现有的调查在这篇文章中。最后，我们提供了一个教程，展示了如何应用一系列的MRRM来分析不同的网络功能如何影响一个动态过程中的经验时间网络。
英文摘要：
Many dynamical systems can be successfully analyzed by representing them as networks. Empirically measured networks and dynamic processes that take place in these situations show heterogeneous, non-Markovian, and intrinsically correlated topologies and dynamics. This makes their analysis particularly challenging. Randomized reference models (RRMs) have emerged as a general and versatile toolbox for studying such systems. Defined as random networks with given features constrained to match those of an input (empirical) network, they may, for example, be used to identify important features of empirical networks and their effects on dynamical processes unfolding in the network. RRMs are typically implemented as procedures that reshuffle an empirical network, making them very generally applicable. However, the effects of most shuffling procedures on network features remain poorly understood, rendering their use nontrivial and susceptible to misinterpretation. Here we propose a unified framework for classifying and understanding microcanonical RRMs (MRRMs) that sample networks with uniform probability. Focusing on temporal networks, we survey applications of MRRMs found in the literature, and we use this framework to build a taxonomy of MRRMs that proposes a canonical naming convention, classifies them, and deduces their effects on a range of important network features. We furthermore show that certain classes of MRRMs may be applied in sequential composition to generate new MRRMs from the existing ones surveyed in this article. We finally provide a tutorial showing how to apply a series of MRRMs to analyze how different network features affect a dynamic process in an empirical temporal network.
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