Detection of Influential Nodes in Network Dynamical Systems from Time-Series

Pietro Delellis, Maurizio Porfiri

Research output: Contribution to journalArticlepeer-review

Abstract

Identifying influential nodes in network systems requires the manipulation of tolopogical and dynamic characteristics within ideal experiments. However, seldom do we have access to experimental settings that could afford targeted interventions or calibrated mathematical models supporting faithful what/if analyses. Our knowledge of the network dynamical system is often limited to the time-series of individual nodes in some real experiment. In the context of synchronization problems, we show that time-series of real experiments can be used to pinpoint causal influence within the network, where the influence of a node is defined as the extent to which adding noise at that particular node affects the overall network synchronization. For linear time-invariant dynamics and undirected topologies, we demonstrate the possibility of detecting the most influential nodes in the network without a calibrated mathematical model, using only time-series of a real experiment where noise plagues all nodes. Beyond illustrating our results on classical and second-order consensus protocols, we consider two real-world datasets: firearm prevalence in the U.S. and players movements in a

Original languageEnglish (US)
JournalIEEE Transactions on Control of Network Systems
DOIs
StateAccepted/In press - 2021

Keywords

  • Consensus
  • stochastic systems
  • synchronization
  • vulnerability

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications
  • Control and Optimization

Fingerprint Dive into the research topics of 'Detection of Influential Nodes in Network Dynamical Systems from Time-Series'. Together they form a unique fingerprint.

Cite this