Using Viterbi and Kalman to detect topological changes in dynamic networks

John A.W.B. Costanzo, Donatello Materassi, Bruno Sinopoli

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

This paper addresses the issue of determining the causal link structure of a network of dynamical systems from time series. Such problem is relevant in contexts where time series data is abundant but causal relationships are unknown. Complex systems, where interaction can rarely be derived from first principles constitute a main target of this effort. Applications abound in biology, environmental sciences and interconnected infrastructures, to name a few. Current methods are promising in their ability to determine link structure, and even provide guarantees under qualified assumptions; however, they are limited in their ability to track changes in causal structure over time. Such changes may result from external or unmodeled disruptions, such as cyber attacks or the formation or dissolution of business relationships. In this paper we formulate a model for changing networks and show that it is a generalization of a Hidden Markov Model (HMM). We then provide an algorithm capable of detecting topological changes in dynamical networks, and we compute the probability distributions of transition times.

Original languageEnglish (US)
Title of host publication2017 American Control Conference, ACC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5410-5415
Number of pages6
ISBN (Electronic)9781509059928
DOIs
StatePublished - Jun 29 2017
Externally publishedYes
Event2017 American Control Conference, ACC 2017 - Seattle, United States
Duration: May 24 2017May 26 2017

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619

Other

Other2017 American Control Conference, ACC 2017
Country/TerritoryUnited States
CitySeattle
Period5/24/175/26/17

Bibliographical note

Funding Information:
This material is based upon work partially supported by the National Science Foundation under grants no. 1646526 and no. 1638327.

Publisher Copyright:
© 2017 American Automatic Control Council (AACC).

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