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Estimating the Diffusion Source in Complex Networks with Sparse Modeling Method

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Proceedings of 2019 Chinese Intelligent Systems Conference (CISC 2019)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 594))

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Abstract

We consider the problem of estimating both the location of source and the start time of diffusion in complex networks. A sparse modeling method based on Lasso is proposed, under the condition that only a subset of nodes can be observed. Compared with least-squares method, the present approach can give more accurate estimation about the diffusion source. Experiments verify the effectiveness of the proposed method in scale-free (BA) and small-world (WS) networks.

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References

  1. Shah D, Zaman T (2011) Rumors in a network: who’s the culprit? IEEE Trans Inform Theory 57(8):5163–5181. https://doi.org/10.1109/TIT.2011.2158885

    Article  MathSciNet  MATH  Google Scholar 

  2. Comin CH, da Fontoura Costa L (2011) Identifying the starting point of a spreading process in complex networks. Phys Rev E 84(5):056105. https://doi.org/10.1103/PhysRevE.84.056105

    Article  Google Scholar 

  3. Lokhov AY, Mézard M, Ohta H, Zdeborová L (2014) Inferring the origin of an epidemic with a dynamic message-passing algorithm. Phys Rev E 90(1):012801. https://doi.org/10.1103/PhysRevE.90.012801

    Article  Google Scholar 

  4. Brockmann D, Helbing D (2013) The hidden geometry of complex, network-driven contagion phenomena. Science 342(6164):1337–1342. https://doi.org/10.1126/science.1245200

    Article  Google Scholar 

  5. Zhu K, Ying L (2014) A robust information source estimator with sparse observations. Comput Soc Netw 1(3):1–21. https://doi.org/10.1186/s40649-014-0003-2

    Article  Google Scholar 

  6. Louni A, Subbalakshmi K (2014) A two-stage algorithm to estimate the source of information diffusion in social media networks. In: INFOCOM workshops, pp 329–333. https://doi.org/10.1109/INFCOMW.2014.6849253

  7. Jiang J, Sheng W, Yu S, Xiang Y, Zhou W (2016) Rumor source identification in social networks with time-varying topology. IEEE Trans Depend Secure Comput 15:166–179. https://doi.org/10.1109/TDSC.2016.2522436

    Article  Google Scholar 

  8. Shi C, Zhang Q, Chu T (2017) Source identification of network diffusion processes with partial observations. In: Chinese control conference, pp 11296–11300. https://doi.org/10.23919/ChiCC.2017.8029159

  9. Pinto PC, Thiran P, Vetterli M (2012) Locating the source of diffusion in large-scale networks. Phys Rev Lett 109(6):068702. https://doi.org/10.1103/PhysRevLett.109.068702

    Article  Google Scholar 

  10. Shen Z, Cao S, Wang WX, Di Z, Stanley HE (2016) Locating the source of diffusion in complex networks by time-reversal backward spreading. Phys Rev E 93(3):032301. https://doi.org/10.1103/PhysRevE.93.032301

    Article  MathSciNet  Google Scholar 

  11. Farajtabar M, Gomez-Rodriguez M, Du N, Zamani M, Zha H, Song L (2015) Back to the past: source identification in diffusion networks from partially observed cascades. Artif Intell Stat. https://arxiv.org/abs/1501.06582/

  12. Starck JL, Murtagh F, Fadili JM (2010) Sparse image and signal processing: wavelets, curvelets, morphological diversity. Cambridge University Press, Cambridge

    Book  Google Scholar 

  13. Tibshirani R (1996) Regression shrinkage and selection via the lasso. J Roy Stat Soc: Ser B (Methodol) 58(1):267–288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x

    Article  MathSciNet  MATH  Google Scholar 

  14. Bishop CM (2006) Pattern recognition and machine learning. Springer, Heidelberg

    MATH  Google Scholar 

  15. Zhang Y, Jin J, Qing X, Wang B, Wang X (2012) Lasso based stimulus frequency recognition model for SSVEP BCIs. Biomed Signal Process Control 7(2):104–111. https://doi.org/10.1016/j.bspc.2011.02.002

    Article  Google Scholar 

  16. Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393(6684):440. https://doi.org/10.1038/30918

    Article  MATH  Google Scholar 

  17. Barabási AL, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512. https://doi.org/10.1126/science.286.5439.509

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgments

This work was supported by NSFC (No. 61673027), Fundamental Research Funds for the Central Universities in UIBE (CXTD10-05,18QD18), National Basic Research Program of China (973 Program, No. 2012CB821200).

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Authors and Affiliations

  1. College of Engineering, Peking University, Beijing, 100871, China

    Chaoyi Shi & Tianguang Chu

  2. School of Information Technology and Management, University of International Business and Economics, Beijing, 100029, China

    Qi Zhang

Authors
  1. Chaoyi Shi
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  2. Qi Zhang
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  3. Tianguang Chu
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Corresponding author

Correspondence to Tianguang Chu .

Editor information

Editors and Affiliations

  1. Beihang University, Beijing, China

    Yingmin Jia

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Junping Du

  3. University of Science and Technology Beijing, Beijing, China

    Weicun Zhang

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Shi, C., Zhang, Q., Chu, T. (2020). Estimating the Diffusion Source in Complex Networks with Sparse Modeling Method. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2019 Chinese Intelligent Systems Conference. CISC 2019. Lecture Notes in Electrical Engineering, vol 594. Springer, Singapore. https://doi.org/10.1007/978-981-32-9698-5_3

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