Compressed Sensing in Cyber Physical Social Systems

  • Radu GrosuEmail author
  • Elahe Ghalebi K.
  • Ali Movaghar
  • Hamidreza Mahyar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10760)


We overview the main results in Compressed Sensing and Social Networks, and discuss the impact they have on Cyber Physical Social Systems (CPSS), which are currently emerging on top of the Internet of Things. Moreover, inspired by randomized Gossip Protocols, we introduce TopGossip, a new compressed-sensing algorithm for the prediction of the top-k most influential nodes in a social network. TopGossip is able to make this prediction by sampling only a relatively small portion of the social network, and without having any prior knowledge of the network structure itself, except for its set of nodes. Our experimental results on three well-known benchmarks, Facebook, Twitter, and Barabási, demonstrate both the efficiency and the accuracy of the TopGossip algorithm.



This work was partially supported by the following awards: AT-HRSM CPSS/IoT Ecosystem, NSF-Frontiers Cyber-Cardia, US-AFOSR Arrive, EU-Artemis EMC2, EU-Ecsel Semi40, EU-Ecsel Productive 4.0, AT-FWF-NFN RiSE, AT-FWF-LogicCS-DC, AT-FFG Harmonia, AT-FFG Em2Apps, and TUW-CPPS-DK.


  1. 1.
    GE Industrial Internet insights report. Technical report (2015).
  2. 2.
    Bae, J., Kim, S.: Identifying and ranking influential spreaders in complex networks by neighborhood coreness. Phys. A 395, 549–559 (2014)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Barabasi, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Berinde, R., Gilbert, A., Indyk, P., Karloff, H., Strauss, M.: Combining geometry and combinatorics: a unified approach to sparse signal recovery. In: Allerton Conference on Communication, Control, and Computing (2008)Google Scholar
  5. 5.
    Bonacich, P.: Power and centrality: a family of measures. Am. J. Sociol. 92, 1170–1182 (1987)CrossRefGoogle Scholar
  6. 6.
    Borgatti, S.P.: Identifying sets of key players in a social network. Comput. Math. Organ. Theory 12, 21–34 (2006)CrossRefGoogle Scholar
  7. 7.
    Boyd, S., Ghosh, A., Prabhakar, B., Shah, D.: Randomized gossip algorithms. IEEE Trans. Inf. Theory 45(6), 2508–2530 (2006)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Brandes, U.: A faster algorithm for betweenness centrality. J. Math. Sociol. 25, 163–177 (2001)CrossRefGoogle Scholar
  9. 9.
    Candes, E.J., Tao, T.: Decoding by linear programming. IEEE Trans. Inf. Theory 51(12), 4203–4215 (2005)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Cheraghchi, M., Karbasi, A., Mohajer, S., Saligrama, V.: Graph constrained group testing. IEEE Trans. Inf. Theory 58(1), 248–262 (2012)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Costa, L., Rodrigues, F., Travieso, G., Villas-Boas, P.: Characterization of complex networks: a survey. Adv. Phys. 56, 167–242 (2007)CrossRefGoogle Scholar
  12. 12.
    Crovella, M., Kolaczyk, E.: Graph wavelets for spatial traffic analysis. In: Twenty-Second Annual Joint Conference of the IEEE Computer and Communications Societies, vol. 3, pp. 1848–1857. IEEE (2003)Google Scholar
  13. 13.
    Davenport, M., Duarte, M., Eldar, Y., Kutyniok, G.: Introduction to compressed sensing. In: Compressed Sensing: Theory and Applications. Cambridge UP (2012)Google Scholar
  14. 14.
    Donoho, D.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Everett, M., Borgatti, S.P.: Ego network betweenness. Soc. Netw. 27, 31–38 (2005)CrossRefGoogle Scholar
  16. 16.
    Facebook Climbs To 1.59 Billion Users And Crushes Q4 Estimates With 5.8B Revenue (2016).
  17. 17.
    Freeman, L.: A set of measures of centrality based on betweenness. Sociometry 40, 35–41 (1977)CrossRefGoogle Scholar
  18. 18.
    Gephi platform for interactive visualization and exploration of graphs (2017).
  19. 19.
    Hammond, D., Vandergheynst, P., Gribonval, R.: Wavelets on graphs via spectral graph theory. Appl. Comput. Harmon. Anal. 30, 129–150 (2011)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Haupt, J., Bajwa, W., Rabbat, M., Nowak, R.: Compressed sensing for networked data: a different approach to decentralized compression. IEEE Sig. Process. Mag. 25(2), 92–101 (2008)CrossRefGoogle Scholar
  21. 21.
    Huang, X., Vodenska, I., Wang, F., Havlin, S., Stanley, H.E.: Identifying influential directors in the United States corporate governance network. Phys. Rev. E 84 (2011)Google Scholar
  22. 22.
    Lee, M., Choi, S., Chung, C.: Efficient algorithms for updating betweenness centrality in fully dynamic graphs. Inf. Sci. 326, 278–296 (2016)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Liu, J.G., Ren, Z.M., Guo, Q.: Ranking the spreading influence in complex networks. Phys. A 392, 4154–4159 (2013)CrossRefGoogle Scholar
  24. 24.
    Lu, L., Zhou, T., Zhang, Q.M., Stanley, H.: The h-index of a network node and its relation to degree and coreness. Nat. Commun. 7, 10168 (2016)CrossRefGoogle Scholar
  25. 25.
    Mackenzie, D.: Compressed sensing makes every pixel count. What’s Happening Math. Sci. 7, 114–127 (2009)Google Scholar
  26. 26.
    Mahyar, H.: Detection of top-k central nodes in social networks: a compressive sensing approach. In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM, pp. 902–909, August 2015Google Scholar
  27. 27.
    Mahyar, H., Rabiee, H.R., Hashemifar, Z.S.: UCS-NT: an unbiased compressive sensing framework for network tomography. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 4534–4538 (2013)Google Scholar
  28. 28.
    Mahyar, H., Rabiee, H.R., Hashemifar, Z.S., Siyari, P.: UCS-WN: an unbiased compressive sensing framework for weighted networks. In: Conference on Information Sciences and Systems, CISS. pp. 1–6, March 2013Google Scholar
  29. 29.
    Mahyar, H., Rabiee, H.R., Movaghar, A., Ghalebi, E., Nazemian, A.: CS-ComDet: a compressive sensing approach for inter-community detection in social networks. In: IEEE/ACM ASONAM, pp. 89–96, August 2015Google Scholar
  30. 30.
    Mahyar, H., Rabiee, H.R., Movaghar, A., Hasheminezhad, R., Ghalebi, E., Nazemian, A.: A low-cost sparse recovery framework for weighted networks under compressive sensing. In: IEEE International Conference on Social Computing and Networking, SocialCom, pp. 183–190, December 2015Google Scholar
  31. 31.
    Mallat, S., Zhang, Z.: Matching pursuits with time-frequency dictionaries. IEEE Trans. Sig. Process. 41(12), 3397–3415 (1993)CrossRefGoogle Scholar
  32. 32.
    Newman, S.: Networks: An introduction, 1st edn. Oxford University Press, Oxford (2010)CrossRefGoogle Scholar
  33. 33.
    Opsahl, T., Panzarasa, P.: Clustering in weighted networks. Soc. Netw. 31(2), 155–163 (2009)CrossRefGoogle Scholar
  34. 34.
    Püschel, M., Moura, J.: Algebraic signal processing theory. arXiv/0612077v1, pp. 1–67 (2006)Google Scholar
  35. 35.
    Sabidussi, G.: The centrality index of a graph. Psychometrika 31, 581–603 (1966)MathSciNetCrossRefGoogle Scholar
  36. 36.
    Sandryhaila, A., Moura, J.: Discrete signal processing on graphs: frequency analysis. IEEE Trans. Sig. Process. 62(12), 3042–3054 (2014)MathSciNetCrossRefGoogle Scholar
  37. 37.
    Singh, B., Gupte, N.: Congestion and decongestion in a communication network. Phys. Rev. E 71(5), 055103 (2005)CrossRefGoogle Scholar
  38. 38.
    Sipser, M., Spielman, D.: Expander codes. IEEE Trans. Inf. Theory 42(6), 1710–1722 (1996)MathSciNetCrossRefGoogle Scholar
  39. 39.
    Tibshirani, R.: Regression shrinkage and selection via the LASSO. J. R. Stat. Soc. B 58, 267–288 (1994)MathSciNetzbMATHGoogle Scholar
  40. 40.
    Xu, S., Wang, P.: Identifying important nodes by adaptive leaderrank. Phys. A 469, 654–664 (2017)CrossRefGoogle Scholar
  41. 41.
    Xu, W., Mallada, E., Tang, A.: Compressive sensing over graphs. In: IEEE INFOCOM, pp. 2087–2095, April 2011Google Scholar
  42. 42.
    Zhu, X., Rabbat, M.: Approximating signals supported on graphs. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3921–3924 (2012)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Radu Grosu
    • 1
    Email author
  • Elahe Ghalebi K.
    • 1
  • Ali Movaghar
    • 2
  • Hamidreza Mahyar
    • 1
  1. 1.Fakultät für InformatikTechnische Universität WienViennaAustria
  2. 2.Department of Computer EngineeringSharif University of TechnologyTehranIran

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