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Adaptive Rumor Spreading

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Web and Internet Economics (WINE 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9470))

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Abstract

Motivated by the recent emergence of the so-called opportunistic communication networks, we consider the issue of adaptivity in the most basic continuous time (asynchronous) rumor spreading process. In our setting a rumor has to be spread to a population; the service provider can push it at any time to any node in the network and has unit cost for doing this. On the other hand, as usual in rumor spreading, nodes share the rumor upon meeting and this imposes no cost on the service provider. Rather than fixing a budget on the number of pushes, we consider the cost version of the problem with a fixed deadline and ask for a minimum cost strategy that spreads the rumor to every node. A non-adaptive strategy can only intervene at the beginning and at the end, while an adaptive strategy has full knowledge and intervention capabilities. Our main result is that in the homogeneous case (where every pair of nodes randomly meet at the same rate) the benefit of adaptivity is bounded by a constant. This requires a subtle analysis of the underlying random process that is of interest in its own right.

Supported by the Millennium Nucleus Information and Coordination in Networks ICM/FIC RC130003, CONICYT via Basal in Applied Mathematics, and an NWO Veni grant.

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References

  1. Andersson, H., Britton, T.: Stochastic Epidemic Models and Their Statistical Analysis. Lecture Notes in Statistics. Springer, New York (2000)

    Book  Google Scholar 

  2. Asadpour, A., Nazerzadeh, H., Saberi, A.: Stochastic submodular maximization. In: Papadimitriou, C., Zhang, S. (eds.) WINE 2008. LNCS, vol. 5385, pp. 477–489. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  3. Bailey, N.: A simple stochastic epidemic. Biometrika 37, 193–202 (1950)

    Article  MathSciNet  Google Scholar 

  4. Barry, K.: Ford bets the fiesta on social networking, September 2009. www.wired.com/2009/04/how-the-fiesta/

  5. Bartlett, M.: An Introduction to Stochastic Processes, with Special Reference to Methods and Applications. Cambridge University Press, Cambridge (1978)

    Google Scholar 

  6. Bollobás, B., Kohayakawa, Y.: On Richardson’s model on the hypercube. In: Bollobás, B., Thomason, A. (eds.) Combinatorics, Geometry and Probability. Cambridge University Press, Cambridge (1997)

    Chapter  Google Scholar 

  7. Borgs, C., Brautbar, M., Chayes, J., Lucier, B.: Maximizing social influence in nearly optimal time. In: SODA (2014)

    Google Scholar 

  8. Boyd, S., Arpita, G., Prabhakar, B., Shah, D.: Randomized gossip algorithms. IEEE Trans. Inf. Theory 52(6), 2508–2530 (2006)

    Article  MathSciNet  Google Scholar 

  9. Chen, N.: On the approximability of influence in social networks. In: SODA (2008)

    Google Scholar 

  10. Chen, W., Wang, C., Wang, Y.: Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: KDD (2010)

    Google Scholar 

  11. Chen, Y., Krause, A.: Near-optimal batch mode active learning and adaptive submodular optimization. In: ICML (2013)

    Google Scholar 

  12. Cisco: VNI: Global mobile data traffic forecast update, 2013–2018 (2014). www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/white_paper_c11-520862.html. Accessed 28 October 2014

  13. Doerr, B., Künnemann, M.: Tight analysis of randomized rumor spreading in complete graphs. In: ANALCO (2014)

    Google Scholar 

  14. Domingos, P., Richardson, M.: Mining the network value customers. In: KDD (2001)

    Google Scholar 

  15. Golovin, D., Krause, A.: Adaptive submodularity: theory and applications in active learning and stochastic optimization. J. Artif. Intell. Res. 42, 427–486 (2011)

    MathSciNet  Google Scholar 

  16. Grimmett, G.: Probability on Graphs: Random Processes on Graphs and Lattices. Cambridge University Press, Cambridge (2010). Institute of Mathematical Statistics Textbooks

    Book  Google Scholar 

  17. Han, B., Hui, P., Kumar, A., Marathe, M., Pei, G., Srinivasan, A.: Cellular traffic offloading through opportunistic communications: a case study. In: CHANTS (2010)

    Google Scholar 

  18. Horel, T., Singer, Y.: Scalable methods for adaptively seeding a social network. In: WWW (2015)

    Google Scholar 

  19. Ioannidis, S., Chaintreau, A., Massoulie, L.: Optimal and scalable distribution of content updates over a mobile social network. In: IEEE INFOCOM (2009)

    Google Scholar 

  20. Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: SIGKDD (2003)

    Google Scholar 

  21. Kempe, D., Kleinberg, J., Tardos, E.: Influential nodes in a diffusion model. In: ICALP (2005)

    Google Scholar 

  22. Kleinberg, J.: Cascading behavior in social and economic networks. In: ACM EC (2013)

    Google Scholar 

  23. Sciancalepore, V., Giustiniano, D., Banchs, A., Picu, A.: Offloading cellular traffic through opportunistic communications: analysis and optimization. ArXiv preprint 1405.3548 (2014)

  24. Seeman, L., Singer, Y.: Adaptive seeding in social networks. In: FOCS (2013)

    Google Scholar 

  25. Whitbeck, J., Amorim, M., Lopez, Y., Leguay, J., Conan, V.: Relieving the wireless infrastructure: When opportunistic networks meet guaranteed delays. In: WOWMOM (2011)

    Google Scholar 

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Acknowledgments

We thank Albert Banchs, Antonio Fernández, Domenico Giustiniano, Nicole Immorlica, Julia Komjáthy, Brendan Lucier, and Yaron Singer for stimulating discussions and helpful pointers to the literature.

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Correspondence to Marcos Kiwi .

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Correa, J., Kiwi, M., Olver, N., Vera, A. (2015). Adaptive Rumor Spreading. In: Markakis, E., Schäfer, G. (eds) Web and Internet Economics. WINE 2015. Lecture Notes in Computer Science(), vol 9470. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48995-6_20

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  • DOI: https://doi.org/10.1007/978-3-662-48995-6_20

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  • Publisher Name: Springer, Berlin, Heidelberg

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