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International Conference on Discovery Science

DS 2019: Discovery Science pp 385–395Cite as

Evolving Social Networks Analysis via Tensor Decompositions: From Global Event Detection Towards Local Pattern Discovery and Specification

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11828))

Abstract

Existing approaches for detecting anomalous events in time-evolving networks usually focus on detecting events involving the majority of the nodes, which affect the overall structure of the network. Since events involving just a small subset of nodes usually do not affect the overall structure of the network, they are more difficult to spot. In this context, tensor decomposition based methods usually beat other techniques in detecting global events, but fail at spotting localized event patterns. We tackle this problem by replacing the batch decomposition with a sliding window decomposition, which is further mined in an unsupervised way using statistical tools. Via experimental results in one synthetic and four real-world networks, we show the potential of the proposed method in the detection and specification of local events.

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References

  1. Akoglu, L., Tong, H., Koutra, D.: Graph based anomaly detection and description: a survey. Data Min. Knowl. Disc. 29(3), 626–688 (2015)

    Article  MathSciNet  Google Scholar 

  2. Berlingerio, M., Koutra, D., Eliassi-Rad, T., Faloutsos, C.: Network similarity via multiple social theories. In: 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 1439–1440. IEEE (2013)

    Google Scholar 

  3. Chi, E.C., Kolda, T.G.: On tensors, sparsity, and nonnegative factorizations. SIAM J. Matrix Anal. Appl. 33(4), 1272–1299 (2012)

    Article  MathSciNet  Google Scholar 

  4. Costa, P.: Online Network Analysis of Stock Markets. Master’s thesis, University of Porto (2018)

    Google Scholar 

  5. Dawson, R.: How significant is a boxplot outlier? J. Stat. Educ. 19(2) (2011)

    Google Scholar 

  6. Desmier, E., Plantevit, M., Robardet, C., Boulicaut, J.-F.: Cohesive co-evolution patterns in dynamic attributed graphs. In: Ganascia, J.-G., Lenca, P., Petit, J.-M. (eds.) DS 2012. LNCS (LNAI), vol. 7569, pp. 110–124. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33492-4_11

    Chapter  Google Scholar 

  7. Fanaee-T, H., Gama, J.: Event detection from traffic tensors: a hybrid model. Neurocomputing 203, 22–33 (2016)

    Article  Google Scholar 

  8. Isella, L., Stehlé, J., Barrat, A., Cattuto, C., Pinton, J.F., Van den Broeck, W.: What’s in a crowd? Analysis of face-to-face behavioral networks. J. Theor. Biol. 271(1), 166–180 (2011). http://www.sociopatterns.org/datasets/infectious-sociopatterns-dynamic-contact-networks/

    Article  MathSciNet  Google Scholar 

  9. Kapsabelis, K.M., Dickinson, P.J., Dogancay, K.: Investigation of graph edit distance cost functions for detection of network anomalies. ANZIAM J. 48, 436–449 (2007)

    Article  MathSciNet  Google Scholar 

  10. Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Rev. 51(3), 455–500 (2009)

    Article  MathSciNet  Google Scholar 

  11. Koutra, D., Papalexakis, E.E., Faloutsos, C.: TensorSplat: spotting latent anomalies in time. In: Proceedings of the 2012 16th Panhellenic Conference on Informatics, PCI 2012, pp. 144–149. IEEE Computer Society (2012)

    Google Scholar 

  12. Michalski, R., Palus, S., Kazienko, P.: Matching organizational structure and social network extracted from email communication. In: Abramowicz, W. (ed.) BIS 2011. LNBIP, vol. 87, pp. 197–206. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21863-7_17

    Chapter  Google Scholar 

  13. Papalexakis, E., Pelechrinis, K., Faloutsos, C.: Spotting misbehaviors in location-based social networks using tensors. In: Proceedings of the Companion Publication of the 23rd International Conference On World Wide Web Companion, pp. 551–552. International World Wide Web Conferences Steering Committee (2014)

    Google Scholar 

  14. Priebe, C.E., Conroy, J.M., Marchette, D.J., Park, Y.: Scan statistics on Enron graphs. Comput. Math. Organ. Theory 11(3), 229–247 (2005)

    Article  Google Scholar 

  15. Ranshous, S., Shen, S., Koutra, D., Harenberg, S., Faloutsos, C., Samatova, N.F.: Anomaly detection in dynamic networks: a survey. Wiley Interdisc. Rev.: Comput. Stat. 7(3), 223–247 (2015)

    Article  MathSciNet  Google Scholar 

  16. Rayana, S., Akoglu, L.: An ensemble approach for event detection and characterization in dynamic graphs. In: ACM SIGKDD ODD Workshop (2014)

    Google Scholar 

  17. Rayana, S., Akoglu, L.: Less is more: building selective anomaly ensembles. ACM Trans. Knowl. Discov. Data (TKDD) 10(4), 42 (2016)

    Google Scholar 

Download references

Acknowledgments

This work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia within project: UID/EEA/50014/2019. Sofia Fernandes also acknowledges the support of FCT via the PhD grant PD/BD/114189/2016. The authors also acknowledge the SocioPatterns collaboration for making the dataset (in [8]) available.

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Authors and Affiliations

  1. LIAAD - INESC TEC, University of Porto, Porto, Portugal

    Sofia Fernandes & João Gama

  2. Department of Biostatistics, University of Oslo, Oslo, Norway

    Hadi Fanaee-T

Authors
  1. Sofia Fernandes
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  2. Hadi Fanaee-T
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  3. João Gama
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Corresponding author

Correspondence to Sofia Fernandes .

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Editors and Affiliations

  1. Jožef Stefan Institute, Ljubljana, Slovenia

    Petra Kralj Novak

  2. Rudjer Bošković Institute, Zagreb, Croatia

    Tomislav Šmuc

  3. Jožef Stefan Institute, Ljubljana, Slovenia

    Sašo Džeroski

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Fernandes, S., Fanaee-T, H., Gama, J. (2019). Evolving Social Networks Analysis via Tensor Decompositions: From Global Event Detection Towards Local Pattern Discovery and Specification. In: Kralj Novak, P., Šmuc, T., Džeroski, S. (eds) Discovery Science. DS 2019. Lecture Notes in Computer Science(), vol 11828. Springer, Cham. https://doi.org/10.1007/978-3-030-33778-0_29

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  • DOI: https://doi.org/10.1007/978-3-030-33778-0_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33777-3

  • Online ISBN: 978-3-030-33778-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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