共识型网络层析成像的筛法
A Sieve Method for Consensus-type Network Tomography
AI总结：
针对共识型算法协调的多智能体系统，提出一种通过端口激励和观测网络响应、利用图拉普拉斯谱和基于度的图重构方法来识别网络交互几何的筛法。
摘要：
本文研究了在采用共识型算法进行协调的已知数量智能体中，识别其交互几何的问题。所提出的识别过程通过引入“端口”来刺激网络顶点子集（通过适当定义的接口）并观测另一组顶点处的网络响应来促进。首先注意到，在相应受控和观测网络的可控性和可观性假设下，所提出的过程利用图拉普拉斯谱识别了网络的若干重要特征。然后，我们利用基于度的图重构方法，提出了一种筛法以进一步刻画底层网络。一个例子展示了所提方法的应用。
英文摘要：
In this note, we examine the problem of identifying the interaction geometry among a known number of agents, adopting a consensus-type algorithm for their coordination. The proposed identification process is facilitated by introducing "ports" for stimulating a subset of network vertices via an appropriately defined interface and observing the network's response at another set of vertices. It is first noted that under the assumption of controllability and observability of corresponding steered-and-observed network, the proposed procedure identifies a number of important features of the network using the spectrum of the graph Laplacian. We then proceed to use degree-based graph reconstruction methods to propose a sieve method for further characterization of the underlying network. An example demonstrates the application of the proposed method.
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。
