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首页> 外文期刊>IEEE Signal Processing Magazine >Connecting the Dots: Identifying Network Structure via Graph Signal Processing
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Connecting the Dots: Identifying Network Structure via Graph Signal Processing

机译：连接点：通过图形信号处理识别网络结构

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摘要

Network topology inference is a significant problem in network science. Most graph signal processing (GSP) efforts to date assume that the underlying network is known and then analyze how the graph's algebraic and spectral characteristics impact the properties of the graph signals of interest. Such an assumption is often untenable beyond applications dealing with, e.g., directly observable social and infrastructure networks; and typically adopted graph construction schemes are largely informal, distinctly lacking an element of validation. This article offers an overview of graph-learning methods developed to bridge the aforementioned gap, by using information available from graph signals to infer the underlying graph topology. Fairly mature statistical approaches are surveyed first, where correlation analysis takes center stage along with its connections to covariance selection and high-dimensional regression for learning Gaussian graphical models. Recent GSP-based network inference frameworks are also described, which postulate that the network exists as a latent underlying structure and that observations are generated as a result of a network process defined in such a graph. A number of arguably more nascent topics are also briefly outlined, including inference of dynamic networks and nonlinear models of pairwise interaction, as well as extensions to directed (di) graphs and their relation to causal inference. All in all, this article introduces readers to challenges and opportunities for SP research in emerging topic areas at the crossroads of modeling, prediction, and control of complex behavior arising in networked systems that evolve over time.
机译：网络拓扑推论是网络科学的重大问题。大多数图表信号处理（GSP）迄今为止的努力假设底层网络是已知的，然后分析图形的代数和光谱特性如何影响感兴趣的图表信号的性质。这种假设通常远远超出了处理的应用，例如，直接可观察到的社会和基础设施网络;通常采用的图形建设方案在很大程度上是非正式的，明显缺乏验证的元素。本文通过使用图形信号可获得的信息来推断出底层图拓扑，提供开发的图形学习方法概述以弥合上述间隙。首先调查相当成熟的统计方法，其中相关性分析沿着中心阶段与其与协方差选择和高斯图形模型的高维回归的联系。还描述了最近基于GSP的网络推断框架，其假设网络存在作为潜在的基础结构，并且由于在这种图形中定义的网络处理而产生观察。还简要概述了许多可以说的更加新生的主题，包括对动态网络和非线性交互的非线性模型的推断，以及指向（DI）图的扩展及其与因果推断的关系。总而言之，本文介绍了在建模，预测和控制在网络系统中产生的复杂行为的交叉路上的新兴主题领域SP研究的挑战和机会的挑战和机会。

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  • 来源
    《IEEE Signal Processing Magazine》 |2019年第3期|16-43|共28页
  • 作者

    Mateos Gonzalo; Segarra Santiago; Marques Antonio G.; Ribeiro Alejandro;

  • 作者单位

    Univ Rochester Dept Elect & Comp Engn Rochester NY 14627 USA|Univ Rochester Goergen Inst Data Sci Rochester NY 14627 USA;

    MIT Inst Data Syst & Soc 77 Massachusetts Ave Cambridge MA 02139 USA|Rice Univ Dept Elect & Comp Engn Houston TX 77251 USA;

    King Juan Carlos Univ Dept Signal Theory & Commun Madrid Spain;

    Univ Minnesota Dept Elect & Comp Engn Minneapolis MN USA;

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