多层网络中尺度结构生成模型的构建框架
A Framework for the Construction of Generative Models for Mesoscale Structure in Multilayer Networks
摘要：
多层网络允许人们表示不同的和耦合的连接模式-例如，时间依赖性、多个子系统或两者都有-这些问题出现在许多应用中，并且难以或难以纳入标准网络表示中。在多层网络的研究中，重要的是研究中尺度（即，中间尺度）结构，例如称为社区的密集节点集，以发现在微观尺度或宏观尺度上不明显的网络特征。中尺度结构的不明确性质及其在经验网络中的普遍存在，使得开发能够产生经验网络中遇到的特征的生成模型至关重要。这种生成模型的主要目的包括生成具有感兴趣的经验属性的合成网络，对中尺度检测方法和算法进行基准测试，以及推断经验多层网络中的结构。在本文中，我们介绍了一个框架的多层网络中尺度结构的生成模型的建设。我们的框架提供了一套标准化的生成模型，以及一套相关的原则，从它们派生，多层网络中的中尺度结构的研究。它统一和推广了许多现有的完全有序的中尺度结构模型（例如，时间的）和无序的（例如，多路复用）多层网络。人们也可以使用它来构建部分有序多层网络中的中尺度结构的生成模型（例如，时间和多路复用的网络）。我们的框架有能力产生经验多层网络的许多功能，它明确地结合了用户指定的层之间的依赖结构。
英文摘要：
Multilayer networks allow one to represent diverse and coupled connectivity patterns --- e.g., time-dependence, multiple subsystems, or both --- that arise in many applications and which are difficult or awkward to incorporate into standard network representations. In the study of multilayer networks, it is important to investigate mesoscale (i.e., intermediate-scale) structures, such as dense sets of nodes known as communities, to discover network features that are not apparent at the microscale or the macroscale. The ill-defined nature of mesoscale structure and its ubiquity in empirical networks make it crucial to develop generative models that can produce the features that one encounters in empirical networks. Key purposes of such generative models include generating synthetic networks with empirical properties of interest, benchmarking mesoscale-detection methods and algorithms, and inferring structure in empirical multilayer networks. In this paper, we introduce a framework for the construction of generative models for mesoscale structures in multilayer networks. Our framework provides a standardized set of generative models, together with an associated set of principles from which they are derived, for studies of mesoscale structures in multilayer networks. It unifies and generalizes many existing models for mesoscale structures in fully-ordered (e.g., temporal) and unordered (e.g., multiplex) multilayer networks. One can also use it to construct generative models for mesoscale structures in partially-ordered multilayer networks (e.g., networks that are both temporal and multiplex). Our framework has the ability to produce many features of empirical multilayer networks, and it explicitly incorporates a user-specified dependency structure between layers.
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。
