Quickest Detection of Dynamic Events in Networks

Shaofeng Zou, Venugopal V. Veeravalli, Jian Li, Don Towsley

Research output: Contribution to journalArticlepeer-review


The problem of quickest detection of dynamic events in networks is studied. At some unknown time, an event occurs, and a number of nodes in the network are affected by the event, in that they undergo a change in the statistics of their observations. It is assumed that the event is dynamic, in that it can propagate along the edges in the network, and affect more and more nodes with time. The event propagation dynamics is assumed to be unknown. The goal is to design a sequential algorithm that can detect a 'significant' event, i.e., when the event has affected no fewer than \eta nodes, as quickly as possible, while controlling the false alarm rate. Fully connected networks are studied first, and the results are then extended to arbitrarily connected networks. The designed algorithms are shown to be adaptive to the unknown propagation dynamics, and their first-order asymptotic optimality is demonstrated as the false alarm rate goes to zero. The algorithms can be implemented with linear computational complexity in the network size at each time step, which is critical for online implementation. Numerical simulations are provided to validate the theoretical results.

Original languageEnglish (US)
Article number8876873
Pages (from-to)2280-2295
Number of pages16
JournalIEEE Transactions on Information Theory
Issue number4
StatePublished - Apr 2020
Externally publishedYes


  • Adaptive algorithms
  • Network-CuSum (N-CuSum)
  • Spartan-CuSum (S-CuSum)
  • asymptotic optimality
  • event propagation
  • structured network

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences


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