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Least Squares Method for Diffusion Source Localization in Complex Networks

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 693))

Abstract

Studying diffusion process in complex networks has become an important issue nowadays. This issue has been addressed for different objectives, including quickly detecting the diffusion outbreak, blocking the propagation, and localizing the diffusion source. In this paper, we are mainly interested in developing an efficient algorithm to estimate both the source and the start time of the diffusion, under the constraint that only a subset of nodes can be observed. In doing so, we use the Ordinary Least Squares method (OLS) on the data gathered at observers, taking advantage of the linear correlation between the relative infection time of a node and its effective distance from the source (Brockman [2]). The proposed algorithm ensures an estimation at few hops from the actual source. We show its efficiency through numerical simulations on both synthetic and real networks.

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References

  1. Arulselvan, A., Commander, C.W., Elefteriadou, L., Pardalos, P.M.: Detecting critical nodes in sparse graphs. Computers & Operations Research 36(7), 2193–2200 (2009)

    Google Scholar 

  2. Brockmann, D., Helbing, D.: The hidden geometry of complex, network-driven contagion phenomena. Science 342(6164), 1337–1342 (2013)

    Google Scholar 

  3. Gomez Rodriguez, M., Leskovec, J., Krause, A.: Inferring networks of diffusion and influence. In: Proceedings of the 16th ACM SIGKDD, pp. 1019–1028. ACM (2010)

    Google Scholar 

  4. Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the 9th ACM SIGKDD, pp. 137–146. ACM (2003)

    Google Scholar 

  5. Kuhlman, C.J., Tuli, G., Swarup, S., Marathe, M.V., Ravi, S.: Blocking simple and complex contagion by edge removal. In: ICDM 2013, pp. 399–408. IEEE (2013)

    Google Scholar 

  6. Lalou, M., Tahraoui, M., Kheddouci, H.: Component-cardinality-constrained critical node problem in graphs. Discrete Applied Mathematics 210, 150–163 (2016)

    Google Scholar 

  7. Liu, Y.Y., Slotine, J.J., Barabási, A.L.: Controllability of complex networks. Nature 473(7346), 167–173 (2011)

    Google Scholar 

  8. Lokhov, A.Y., Mézard, M., Ohta, H., Zdeborová, L.: Inferring the origin of an epidemic with a dynamic message-passing algorithm. Physical Review E 90(1), 012,801 (2014)

    Google Scholar 

  9. Louni, A., Subbalakshmi, K.: A two-stage algorithm to estimate the source of information diffusion in social media networks. In: Computer Communications Workshops (INFOCOMWKSHPS), 2014 IEEE Conference on, pp. 329–333. IEEE (2014)

    Google Scholar 

  10. Luo, W., Tay, W.P., Leng, M.: How to identify an infection source with limited observations. Selected Topics in Signal Processing, IEEE Journal of 8(4), 586–597 (2014)

    Google Scholar 

  11. Pinto, P.C., Thiran, P., Vetterli, M.: Locating the source of diffusion in large-scale networks. Physical review letters 109(6), 068,702 (2012)

    Google Scholar 

  12. Rao, C.R., Toutenburg, H.: Linear models. Springer (1995)

    Google Scholar 

  13. Seo, E., Mohapatra, P., Abdelzaher, T.: Identifying rumors and their sources in social networks. In: SPIE defense, security, and sensing, pp. 83,891I–83,891I (2012)

    Google Scholar 

  14. Shah, D., Zaman, T.: Detecting sources of computer viruses in networks: theory and experiment. In: ACM SIGMETRICS Performance Evaluation Review, vol. 38, pp. 203–214. ACM (2010)

    Google Scholar 

  15. Wang, X.F., Chen, G.: Complex networks: small-world, scale-free and beyond. IEEE circuits and systems magazine 3(1), 6–20 (2003)

    Google Scholar 

  16. Zejnilovic, S., Gomes, J., Sinopoli, B.: Network observability and localization of the source of diffusion based on a subset of nodes. In: Communication, Control, and Computing (Allerton), 2013 51st Annual Allerton Conference on, pp. 847–852. IEEE (2013)

    Google Scholar 

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Correspondence to Mohammed Lalou .

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Lalou, M., Kheddouci, H. (2017). Least Squares Method for Diffusion Source Localization in Complex Networks. In: Cherifi, H., Gaito, S., Quattrociocchi, W., Sala, A. (eds) Complex Networks & Their Applications V. COMPLEX NETWORKS 2016 2016. Studies in Computational Intelligence, vol 693. Springer, Cham. https://doi.org/10.1007/978-3-319-50901-3_38

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  • DOI: https://doi.org/10.1007/978-3-319-50901-3_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50900-6

  • Online ISBN: 978-3-319-50901-3

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