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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)

Abstract

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.

Keywords

Change detection Pattern mining 

References

  1. 1.
    Akoglu, L., Tong, H., Koutra, D.: Graph based anomaly detection and description: a survey. Data Min. Knowl. Discov. 29(3), 626–688 (2015)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Bailey, J.: Statistical measures for contrast patterns. In: Contrast Data Mining: Concepts, Algorithms, and Applications, pp. 13–20. CRC Press (2013)Google Scholar
  3. 3.
    Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with drift detection. In: Bazzan, A.L.C., Labidi, S. (eds.) SBIA 2004. LNCS (LNAI), vol. 3171, pp. 286–295. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-28645-5_29CrossRefGoogle Scholar
  4. 4.
    Geerts, F., Goethals, B., Mielikäinen, T.: Tiling databases. In: Proceedings of 7th International Conference Discovery Science, DS 2004, 2–5 October 2004, Padova, Italy, pp. 278–289 (2004)CrossRefGoogle Scholar
  5. 5.
    He, Z., Xu, X., Huang, J.Z., Deng, S.: FP-outlier: frequent pattern based outlier detection. Comput. Sci. Inf. Syst. 2(1), 103–118 (2005)CrossRefGoogle Scholar
  6. 6.
    Koh, Y.S.: CD-TDS: change detection in transactional data streams for frequent pattern mining. In: 2016 International Joint Conference on Neural Networks, IJCNN 2016, 24–29 July 2016, Vancouver, BC, Canada, pp. 1554–1561 (2016)Google Scholar
  7. 7.
    Koufakou, A., Secretan, J., Georgiopoulos, M.: Non-derivable itemsets for fast outlier detection in large high-dimensional categorical data. Knowl. Inf. Syst. 29(3), 697–725 (2011)CrossRefGoogle Scholar
  8. 8.
    van Leeuwen, M., Siebes, A.: Streamkrimp: detecting change in data streams. In: Proceedings of European Conference on Machine Learning and Knowledge Discovery in Databases (Part I), ECML/PKDD 2008, 15–19 September 2008, Antwerp, Belgium, pp. 672–687 (2008)Google Scholar
  9. 9.
    Loglisci, C., Ceci, M., Impedovo, A., Malerba, D.: Mining microscopic and macroscopic changes in network data streams. Knowl. Based Syst. 161, 294–312 (2018)CrossRefGoogle Scholar
  10. 10.
    Padillo, F., Luna, J.M., Ventura, S.: Subgroup discovery on big data: pruning the search space on exhaustive search algorithms. In: 2016 IEEE International Conference on Big Data, BigData 2016, 5–8 December 2016, Washington DC, USA, pp. 1814–1823 (2016)Google Scholar
  11. 11.
    Trabold, D., Horváth, T.: Mining strongly closed itemsets from data streams. In: Yamamoto, A., Kida, T., Uno, T., Kuboyama, T. (eds.) DS 2017. LNCS (LNAI), vol. 10558, pp. 251–266. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-67786-6_18CrossRefGoogle Scholar

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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