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 language||English (US)|
|Title of host publication||2017 American Control Conference, ACC 2017|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|State||Published - Jun 29 2017|
|Event||2017 American Control Conference, ACC 2017 - Seattle, United States|
Duration: May 24 2017 → May 26 2017
|Name||Proceedings of the American Control Conference|
|Other||2017 American Control Conference, ACC 2017|
|Period||5/24/17 → 5/26/17|
Bibliographical noteFunding Information:
This material is based upon work partially supported by the National Science Foundation under grants no. 1646526 and no. 1638327.
© 2017 American Automatic Control Council (AACC).