Department of Engineering Mathematics, Merchant Venturers Building, University of Bristol, Woodland Road, Clifton, Bristol, BS8 1UB, United Kingdom.
Institute of Innovative Research, Tokyo Institute of Technology, Nagatsuta-cho 4259, Midori-ku, Yokohama, Kanagawa, 226-8503, Japan.
Sci Rep. 2019 Jan 28;9(1):795. doi: 10.1038/s41598-018-37534-2.
Many time-evolving systems in nature, society and technology leave traces of the interactions within them. These interactions form temporal networks that reflect the states of the systems. In this work, we pursue a coarse-grained description of these systems by proposing a method to assign discrete states to the systems and inferring the sequence of such states from the data. Such states could, for example, correspond to a mental state (as inferred from neuroimaging data) or the operational state of an organization (as inferred by interpersonal communication). Our method combines a graph distance measure and hierarchical clustering. Using several empirical data sets of social temporal networks, we show that our method is capable of inferring the system's states such as distinct activities in a school and a weekday state as opposed to a weekend state. We expect the methods to be equally useful in other settings such as temporally varying protein interactions, ecological interspecific interactions, functional connectivity in the brain and adaptive social networks.
许多自然、社会和技术中的时变系统都会留下系统内部相互作用的痕迹。这些相互作用形成了反映系统状态的时间网络。在这项工作中，我们通过提出一种将离散状态分配给系统并从数据中推断这些状态序列的方法，对这些系统进行粗粒度描述。例如，这些状态可以对应于精神状态（如从神经影像学数据推断出的状态）或组织的运行状态（如通过人际交流推断出的状态）。我们的方法结合了图距离度量和层次聚类。使用几个社会时间网络的经验数据集，我们表明我们的方法能够推断出系统的状态，例如学校中的不同活动，以及工作日状态与周末状态。我们预计该方法在其他环境中同样有用，例如时间变化的蛋白质相互作用、生态种间相互作用、大脑中的功能连接和适应性社会网络。
