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摘要翻译：
本文探讨了如何利用网络中心性和网络熵来识别一个分岔网络事件。当网络因内部变化或外部信号而发生结构上的质变时，往往会出现分叉现象。在本文中，我们证明了网络中心性允许我们捕捉动态网络的重要拓扑性质。通过从网络中提取多个中心性特征进行降维，我们能够跟踪内在低维流形下的网络动力学。此外，我们利用冯诺依曼图熵(VNGE)来度量网络之间的信息随时间的差异。特别地，我们提出了VNGE的渐近相合估计，使得VNGE的三次复杂度降低到二次复杂度，使得VNGE的复杂度随网络规模的变化而变化。最后，通过一个网络入侵检测的实际应用，验证了该方法的有效性。
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英文标题：
《First-order bifurcation detection for dynamic complex networks》
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作者：
Sijia Liu, Pin-Yu Chen, Indika Rajapakse, Alfred Hero
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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：
  In this paper, we explore how network centrality and network entropy can be used to identify a bifurcation network event. A bifurcation often occurs when a network undergoes a qualitative change in its structure as a response to internal changes or external signals. In this paper, we show that network centrality allows us to capture important topological properties of dynamic networks. By extracting multiple centrality features from a network for dimensionality reduction, we are able to track the network dynamics underlying an intrinsic low-dimensional manifold. Moreover, we employ von Neumann graph entropy (VNGE) to measure the information divergence between networks over time. In particular, we propose an asymptotically consistent estimator of VNGE so that the cubic complexity of VNGE is reduced to quadratic complexity that scales more gracefully with network size. Finally, the effectiveness of our approaches is demonstrated through a real-life application of cyber intrusion detection.
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PDF链接：
https://arxiv.org/pdf/1802.0625

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关键词：复杂网络 Applications Optimization Application bifurcation 我们 发生 维流形 能够 允许