Su Ri-Qi, Wang Wen-Xu, Lai Ying-Cheng
School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, Arizona 85287, USA.
Phys Rev E Stat Nonlin Soft Matter Phys. 2012 Jun;85(6 Pt 2):065201. doi: 10.1103/PhysRevE.85.065201. Epub 2012 Jun 29.
We develop a general method to detect hidden nodes in complex networks, using only time series from nodes that are accessible to external observation. Our method is based on compressive sensing and we formulate a general framework encompassing continuous- and discrete-time and the evolutionary-game type of dynamical systems as well. For concrete demonstration, we present an example of detecting hidden nodes from an experimental social network. Our paradigm for detecting hidden nodes is expected to find applications in a variety of fields where identifying hidden or black-boxed objects based on a limited amount of data is of interest.
我们开发了一种通用方法来检测复杂网络中的隐藏节点，该方法仅使用外部可观测节点的时间序列。我们的方法基于压缩感知，并且我们制定了一个通用框架，该框架涵盖连续时间和离散时间以及动态系统的进化博弈类型。为了具体说明，我们给出了一个从实验性社交网络中检测隐藏节点的示例。我们检测隐藏节点的范例有望在各种领域找到应用，在这些领域中，基于有限的数据量识别隐藏或黑箱对象是有意义的。
