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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Arulselvan, A., Commander, C.W., Elefteriadou, L., Pardalos, P.M.: Detecting critical nodes in sparse graphs. Computers & Operations Research 36(7), 2193–2200 (2009)
Brockmann, D., Helbing, D.: The hidden geometry of complex, network-driven contagion phenomena. Science 342(6164), 1337–1342 (2013)
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)
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)
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)
Lalou, M., Tahraoui, M., Kheddouci, H.: Component-cardinality-constrained critical node problem in graphs. Discrete Applied Mathematics 210, 150–163 (2016)
Liu, Y.Y., Slotine, J.J., Barabási, A.L.: Controllability of complex networks. Nature 473(7346), 167–173 (2011)
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)
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)
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)
Pinto, P.C., Thiran, P., Vetterli, M.: Locating the source of diffusion in large-scale networks. Physical review letters 109(6), 068,702 (2012)
Rao, C.R., Toutenburg, H.: Linear models. Springer (1995)
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)
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)
Wang, X.F., Chen, G.: Complex networks: small-world, scale-free and beyond. IEEE circuits and systems magazine 3(1), 6–20 (2003)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-50901-3_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-50900-6
Online ISBN: 978-3-319-50901-3
eBook Packages: EngineeringEngineering (R0)