Abstract
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.
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References
GE Industrial Internet insights report. Technical report (2015). www.ge.com/digital/sites/default/files/industrial-internet-insights-report.pdf
Bae, J., Kim, S.: Identifying and ranking influential spreaders in complex networks by neighborhood coreness. Phys. A 395, 549–559 (2014)
Barabasi, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)
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)
Bonacich, P.: Power and centrality: a family of measures. Am. J. Sociol. 92, 1170–1182 (1987)
Borgatti, S.P.: Identifying sets of key players in a social network. Comput. Math. Organ. Theory 12, 21–34 (2006)
Boyd, S., Ghosh, A., Prabhakar, B., Shah, D.: Randomized gossip algorithms. IEEE Trans. Inf. Theory 45(6), 2508–2530 (2006)
Brandes, U.: A faster algorithm for betweenness centrality. J. Math. Sociol. 25, 163–177 (2001)
Candes, E.J., Tao, T.: Decoding by linear programming. IEEE Trans. Inf. Theory 51(12), 4203–4215 (2005)
Cheraghchi, M., Karbasi, A., Mohajer, S., Saligrama, V.: Graph constrained group testing. IEEE Trans. Inf. Theory 58(1), 248–262 (2012)
Costa, L., Rodrigues, F., Travieso, G., Villas-Boas, P.: Characterization of complex networks: a survey. Adv. Phys. 56, 167–242 (2007)
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)
Davenport, M., Duarte, M., Eldar, Y., Kutyniok, G.: Introduction to compressed sensing. In: Compressed Sensing: Theory and Applications. Cambridge UP (2012)
Donoho, D.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)
Everett, M., Borgatti, S.P.: Ego network betweenness. Soc. Netw. 27, 31–38 (2005)
Facebook Climbs To 1.59 Billion Users And Crushes Q4 Estimates With 5.8B Revenue (2016). http://techcrunch.com/2016/01/27/facebook-earnings-q4-2015
Freeman, L.: A set of measures of centrality based on betweenness. Sociometry 40, 35–41 (1977)
Gephi platform for interactive visualization and exploration of graphs (2017). http://rankinfo.pkqs.net/twittercrawl.dot.gz
Hammond, D., Vandergheynst, P., Gribonval, R.: Wavelets on graphs via spectral graph theory. Appl. Comput. Harmon. Anal. 30, 129–150 (2011)
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)
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)
Lee, M., Choi, S., Chung, C.: Efficient algorithms for updating betweenness centrality in fully dynamic graphs. Inf. Sci. 326, 278–296 (2016)
Liu, J.G., Ren, Z.M., Guo, Q.: Ranking the spreading influence in complex networks. Phys. A 392, 4154–4159 (2013)
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)
Mackenzie, D.: Compressed sensing makes every pixel count. What’s Happening Math. Sci. 7, 114–127 (2009)
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 2015
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)
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 2013
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 2015
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 2015
Mallat, S., Zhang, Z.: Matching pursuits with time-frequency dictionaries. IEEE Trans. Sig. Process. 41(12), 3397–3415 (1993)
Newman, S.: Networks: An introduction, 1st edn. Oxford University Press, Oxford (2010)
Opsahl, T., Panzarasa, P.: Clustering in weighted networks. Soc. Netw. 31(2), 155–163 (2009)
Püschel, M., Moura, J.: Algebraic signal processing theory. arXiv/0612077v1, pp. 1–67 (2006)
Sabidussi, G.: The centrality index of a graph. Psychometrika 31, 581–603 (1966)
Sandryhaila, A., Moura, J.: Discrete signal processing on graphs: frequency analysis. IEEE Trans. Sig. Process. 62(12), 3042–3054 (2014)
Singh, B., Gupte, N.: Congestion and decongestion in a communication network. Phys. Rev. E 71(5), 055103 (2005)
Sipser, M., Spielman, D.: Expander codes. IEEE Trans. Inf. Theory 42(6), 1710–1722 (1996)
Tibshirani, R.: Regression shrinkage and selection via the LASSO. J. R. Stat. Soc. B 58, 267–288 (1994)
Xu, S., Wang, P.: Identifying important nodes by adaptive leaderrank. Phys. A 469, 654–664 (2017)
Xu, W., Mallada, E., Tang, A.: Compressive sensing over graphs. In: IEEE INFOCOM, pp. 2087–2095, April 2011
Zhu, X., Rabbat, M.: Approximating signals supported on graphs. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3921–3924 (2012)
Acknowledgments
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.
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Grosu, R., Ghalebi K., E., Movaghar, A., Mahyar, H. (2018). Compressed Sensing in Cyber Physical Social Systems. In: Lohstroh, M., Derler, P., Sirjani, M. (eds) Principles of Modeling. Lecture Notes in Computer Science(), vol 10760. Springer, Cham. https://doi.org/10.1007/978-3-319-95246-8_17
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