Topology Identification of Dynamical Networks via Compressive Sensing

Sina Jahandari, Donatello Materassi

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

In this paper, we address the problem of topology identification of causal dynamical networks. Collecting the outputs of the nodes as time series, without any a priori knowledge about the structure of the network, we propose a data-driven algorithm that unveils the topology of the network and identifies the dynamics of connections. We cast the problem as a structured sparse signal recovery based on concepts borrowed from compressive sensing and matching pursuit. When sufficient data is available, the proposed algorithm results in perfect identification of general networks including feedback and self-loops. For noninvasive data, the proposed algorithm outperforms existing techniques. To demonstrate the effectiveness and advantages of the proposed method, we compare the simulation results with those of the Granger causality and other state-of-the-art techniques. As an empirical application, the proposed algorithm is deployed to construct a graphical network describing the interconnections of 30 companies in the Dow Jones stock market index with their prices ranging from January 2012 to June 2017.

Original languageEnglish (US)
Pages (from-to)575-580
Number of pages6
JournalIFAC-PapersOnLine
Volume51
Issue number15
DOIs
StatePublished - Jan 1 2018
Externally publishedYes

Keywords

  • Causal dynamical networks
  • Compressive sensing
  • Granger causality
  • Matching pursuit
  • Noninvasive measurements

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