摘要翻译：
随机网络通过在网络迭代矩阵特征值中产生不确定性来影响图滤波器的设计。虽然特征值的联合统计通常难以分析，但可预测的谱渐近可以出现在大规模网络中。以前发表的研究成功地分析了由无向图和转置对称分布的有向图描述的大规模网络，重点研究了时不变网络的一致性加速滤波器设计作为一个应用。通过分析某些由转置-非对称分布描述的大规模有向网络，本工作扩展了这些结果。特别地，对于具有节点传递对称群和正规均值矩阵的转置-非对称逾渗网络模型，有效的可计算谱密度近似是可能的。数值模拟支持了推导出的近似，并应用于一致性滤波器。
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英文标题：
《Spectral Statistics of Directed Networks with Random Link Model
  Transpose-Asymmetry》
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作者：
Stephen Kruzick and Jos\'e M. F. Moura
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  Stochastic network influences complicate graph filter design by producing uncertainty in network iteration matrix eigenvalues, the points at which the graph filter response is defined. While joint statistics for the eigenvalues typically elude analysis, predictable spectral asymptotics can emerge for large scale networks. Previously published works successfully analyze large-scale networks described by undirected graphs and directed graphs with transpose-symmetric distributions, focusing on consensus acceleration filter design for time-invariant networks as an application. This work expands upon these results by enabling analysis of certain large-scale directed networks described by transpose-asymmetric distributions. Specifically, efficiently computable spectral density approximations are possible for transpose-asymmetric percolation network models with node-transitive symmetry group and normal mean matrix. Numerical simulations support the derived approximations and application to consensus filters.
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PDF链接：
https://arxiv.org/pdf/1802.10159