Cyber–physical–social systems (CPSS) are physical devices that are embedded in human society and possess highly integrated functionalities of sensing, computing, communication, and control. CPSS rely on their intense collaboration and information sharing through networks to be functioning. In this paper, topology-informed network information dynamics models are proposed to characterize the evolution of information processing capabilities of CPSS nodes in networks. The models are based on a mesoscale probabilistic graph model, where the sensing and computing capabilities of the nodes are captured as the probabilities of correct predictions. A topology-informed vector autoregression model and a latent variable vector autoregression model are proposed to model the correlations between prediction capabilities of nodes as linear functional relationships. A hybrid Gaussian process regression model is also developed to capture both the nonlinear spatial and temporal correlations between nodes. The new information dynamics models are demonstrated and tested with a simulator of CPSS networks. The results show that the topological information of networks can improve the efficiency in constructing the time series models. The network topology also has influences on the prediction capabilities of CPSS.

中文翻译：

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网络-物理-社会系统网络中的拓扑信息动态建模

网络-物理-社会系统（CPSS）是嵌入人类社会的物理设备，具有高度集成的传感、计算、通信和控制功能。CPSS 依靠他们通过网络进行的密切协作和信息共享来发挥作用。在本文中，提出了基于拓扑的网络信息动态模型来表征网络中CPSS节点的信息处理能力的演变。这些模型基于中尺度概率图模型，其中节点的感知和计算能力被捕获为正确预测的概率。提出了一种基于拓扑的向量自回归模型和一种潜在变量向量自回归模型，将节点预测能力之间的相关性建模为线性函数关系。还开发了混合高斯过程回归模型来捕获节点之间的非线性空间和时间相关性。新的信息动力学模型通过 CPSS 网络模拟器进行了演示和测试。结果表明，网络的拓扑信息可以提高时间序列模型的构建效率。网络拓扑对CPSS的预测能力也有影响。新的信息动力学模型通过 CPSS 网络模拟器进行了演示和测试。结果表明，网络的拓扑信息可以提高时间序列模型的构建效率。网络拓扑对CPSS的预测能力也有影响。新的信息动力学模型通过 CPSS 网络模拟器进行了演示和测试。结果表明，网络的拓扑信息可以提高时间序列模型的构建效率。网络拓扑对CPSS的预测能力也有影响。