论文标题
网络结构从损坏的数据流识别
Network Structure Identification from Corrupt Data Streams
论文作者
论文摘要
复杂的网络系统可以建模为图形，其节点代表代理和链接描述它们之间的动态耦合。关于网络识别的先前工作表明，可以从数据流的关节功率谱重建线性时间不变（LTI）系统的网络结构。这些结果假设数据是完美测量的。但是，现实世界中的数据受到许多损坏，例如时间戳记，噪声和数据丢失。我们表明，使用损坏的测量结果识别线性时间不变系统的结构会导致推断错误的链接。我们提供了一个确切的表征，并证明了这种错误的链接仅限于扰动节点的邻域。我们将LTI系统的分析扩展到具有损坏测量值的马尔可夫随机字段的情况。我们表明，马尔可夫随机字段中的数据损坏导致在LTI系统中出现虚假链接的位置，导致虚假的概率关系。
Complex networked systems can be modeled as graphs with nodes representing the agents and links describing the dynamic coupling between them. Previous work on network identification has shown that the network structure of linear time-invariant (LTI) systems can be reconstructed from the joint power spectrum of the data streams. These results assumed that data is perfectly measured. However, real-world data is subject to many corruptions, such as inaccurate time-stamps, noise, and data loss. We show that identifying the structure of linear time-invariant systems using corrupt measurements results in the inference of erroneous links. We provide an exact characterization and prove that such erroneous links are restricted to the neighborhood of the perturbed node. We extend the analysis of LTI systems to the case of Markov random fields with corrupt measurements. We show that data corruption in Markov random fields results in spurious probabilistic relationships in precisely the locations where spurious links arise in LTI systems.
