Efficient and Accurate Non-exhaustive Pattern-Based Change Detection in Dynamic Networks

  • Angelo ImpedovoEmail author
  • Michelangelo Ceci
  • Toon Calders
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11828)


Pattern-based change detectors (PBCDs) are non-parametric unsupervised change detection methods that are based on observed changes in sets of frequent patterns over time. In this paper we study PBCDs for dynamic networks; that is, graphs that change over time, represented as a stream of snapshots. Accurate PBCDs rely on exhaustively mining sets of patterns on which a change detection step is performed. Exhaustive mining, however, has worst case exponential time complexity, rendering this class of algorithms inefficient in practice. Therefore, in this paper we propose non-exhaustive PBCDs for dynamic networks. The algorithm we propose prunes the search space following a beam-search approach. The results obtained on real-world and synthetic dynamic networks, show that this approach is surprisingly effective in both increasing the efficiency of the mining step as in achieving higher detection accuracy, compared with state-of-the-art approaches.


Change detection Pattern mining 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Angelo Impedovo
    • 1
    Email author
  • Michelangelo Ceci
    • 1
  • Toon Calders
    • 2
  1. 1.Department of Computer ScienceUniversity of Bari “Aldo Moro”BariItaly
  2. 2.Department of Computer ScienceUniversity of AntwerpAntwerpBelgium

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