Skip to main content
SpringerLink
Log in

Evangelism in Social Networks

  • Conference paper
  • First Online:
Book cover Combinatorial Algorithms (IWOCA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9843))

Included in the following conference series:

Abstract

We consider a population of interconnected individuals that, with respect to a piece information, at each time instant can be subdivided into three (time-dependent) categories: agnostic, influenced, and evangelists. A dynamical process of information diffusion evolves among the individuals of the population according to the following rules. Initially, all individuals are agnostic. Then, a set of people is chosen from the outside and convinced to start evangelizing, i.e., to start spreading the information. When a number of evangelists, greater than a given threshold, communicate with an node v, the node v becomes influenced, whereas, as soon as the individual v is contacted by a sufficiently much larger number of evangelists, it is itself converted into an evangelist and consequently it starts spreading the information. The question is: How to choose a bounded cardinality initial set of evangelists so as to maximize the final number of influenced individuals? We prove that the problem is hard to solve, even in an approximate sense, and we present exact polynomial time algorithms for trees and complete graphs. For general graphs, we derive exact algorithms parameterized with respect to neighborhood diversity. We also study the problem when the objective is to select a minimum number of evangelists able of influencing the whole network.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    See [15] for definitions of W[2]-hardness, W[1]-hardness and the class XP.

  2. 2.

    Indeed v should be an evangelist, however the budget is 0 while the threshold is \(>0\).

  3. 3.

    For the root node r, the quantities \(\widehat{NO}_{r}[b]\), \(\widehat{Inf}_{r}[b]\) and \(\widehat{Evg}_{r}[b]\) are not required.

References

  1. Ackerman, E., Ben-Zwi, O., Wolfovitz, G.: Combinatorial model and bounds for target set selection. Theor. Comput. Sci. 411, 4017–4022 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  2. Adamic, L.A., Lento, T.M., Adar, E., Ng, P.C.: Information evolution in social networks. In: Proceedings of the 9th ACM WSDM 2016, pp. 473–482 (2016)

    Google Scholar 

  3. Bazgan, C., Chopin, M., Nichterlein, A., Sikora, F.: Parametrized approximability of maximizing the spread of influence in networks. J. Discrete Algorithms 27, 54–65 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  4. Ben-Zwi, O., Hermelin, D., Lokshtanov, D., Newman, I.: Treewidth governs the complexity of target set selection. Discrete Optim. 8, 87–96 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  5. Chen, N.: On the approximability of influence in social networks. SIAM J. Discrete Math. 23, 1400–1415 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  6. Chen, W., Lakshmanan, L.V.S., Castillo, C.: Information and Influence Propagation in Social Networks. Morgan & Claypool, San Rafael (2013)

    Google Scholar 

  7. Centeno, C.C., Dourado, M.C., Draque Penso, L., Rautenbach, D., Szwarcfiter, J.L.: Irreversible conversion of graphs. Theor. Comput. Sci. 412(29), 3693–3700 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  8. Chopin, M., Nichterlein, A., Niedermeier, R., Weller, M.: Constant thresholds can make target set selection tractable. Theor. Comput. Syst. 55, 61–83 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  9. Chiang, C.-Y., Huang, L.-H., Li, B.-J., Wu, J., Yeh, H.-G.: Some results on the target set selection problem. J. Comb. Optim. 25, 702–715 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  10. Cicalese, F., Cordasco, G., Gargano, L., Milanič, M., Vaccaro, U.: Latency-bounded target set selection in social networks. Theor. Comput. Sci. 535, 1–15 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  11. Cicalese, F., Cordasco, G., Gargano, L., Milanič, M., Peters, J.G., Vaccaro, U.: Spread of influence in weighted networks under time and budget constraints. Theor. Comput. Sci. 586, 40–58 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  12. Coja-Oghlan, A., Feige, U., Krivelevich, M., Reichman, D.: Contagious sets in expanders. In: Proceedings of SODA 2015, pp. 1953–1987 (2015)

    Google Scholar 

  13. Cordasco, G., Gargano, L., Mecchia, M., Rescigno, A.A., Vaccaro, U.: A fast and effective heuristic for discovering small target sets in social networks. In: Lu, Z., Kim, D., Wu, W., Li, W., Du, D.-Z. (eds.) Combinatorial Optimization and Applications. LNCS, vol. 9486, pp. 193–208. Springer, Heidelberg (2015)

    Chapter  Google Scholar 

  14. Dawkins, R.: The Selfish Gene. Oxford University Press, Oxford (1989)

    Google Scholar 

  15. Downey, R.G., Fellows, M.R.: Parameterized Complexity. Springer, Heidelberg (2012)

    MATH  Google Scholar 

  16. Easley, D., Kleinberg, J.: Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press, New York (2010)

    Book  MATH  Google Scholar 

  17. Fiala, J., Gavenciak, T., Knop, D., Koutecky, M., Kratochvíl, J.: Fixed parameter complexity of distance constrained labeling and uniform channel assignment problems (2015). arXiv:1507.00640

  18. Freund, D., Poloczek, M., Reichman, D.: Contagious sets in dense graphs. In: Lipták, Z. (ed.) IWOCA 2015. LNCS, vol. 9538, pp. 185–196. Springer, Heidelberg (2016). doi:10.1007/978-3-319-29516-9_16

    Chapter  Google Scholar 

  19. R. Ganian,Using neighborhood diversity to solve hard problems (2012). arXiv:1201.3091

  20. Gargano, L., Rescigno, A.A.: Complexity of conflict-free colorings of graphs. Theor. Comput. Sci. 566, 39–49 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  21. Gargano, L., Hell, P., Peters, J.G., Vaccaro, U.: Influence diffusion in social networks under time window constraints. Theor. Comput. Sci. 584, 53–66 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  22. Kawasaki, G.: Selling the Dream: How to Promote Your Product, Company, or Ideas and Make a Difference Using Everyday Evangelism. HarperCollins, New York (1991)

    Google Scholar 

  23. Kempe, D., Kleinberg, J.M., Tardos, E.: Maximizing the Spread of Influence through a Social Network. Theor. Comput. 11, 105–147 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  24. Lampis, M.: Algorithmic meta-theorems for restrictions of treewidth. Algorithmica 64, 19–37 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  25. Leppaniemi, M., et al.: Targeting young voters in a political campaign: empirical insights into an interactive digital marketing campaign in the 2007 finnish general election. J. Nonprofit Public Sect. Mark. 22, 14–37 (2010)

    Article  Google Scholar 

  26. McConnell, B., Huba, J.: Creating Customer Evangelists: How Loyal Customers Become a Volunteer Sales Force. Lewis Lane Press, USA (2012)

    Google Scholar 

  27. Nichterlein, A., Niedermeier, R., Uhlmann, J., Weller, M.: Tractable cases of target set selection. Soc. Netw. Anal. Min. 3, 1–24 (2012)

    MATH  Google Scholar 

  28. Reddy, T.V.T., Rangan, C.P.: Variants of spreading messages. J. Graph Algorithms Appl. 15(5), 683–699 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  29. Tumulty, K.: Obama’s Viral Marketing Campaign. TIME Magazine, July 5 2007

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Informatics, University of Salerno, Fisciano, Italy

    Luisa Gargano, Adele A. Rescigno & Ugo Vaccaro

  2. Department of Psychology, Second University of Naples, Caserta, Italy

    Gennaro Cordasco

Authors
  1. Gennaro Cordasco
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luisa Gargano
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Adele A. Rescigno
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ugo Vaccaro
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gennaro Cordasco .

Editor information

Editors and Affiliations

  1. University of Helsinki, Helsinki, Finland

    Veli Mäkinen

  2. University of Helsinki, Helsinki, Finland

    Simon J. Puglisi

  3. University of Helsinki, Helsinki, Finland

    Leena Salmela

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Cordasco, G., Gargano, L., Rescigno, A.A., Vaccaro, U. (2016). Evangelism in Social Networks. In: Mäkinen, V., Puglisi, S., Salmela, L. (eds) Combinatorial Algorithms. IWOCA 2016. Lecture Notes in Computer Science(), vol 9843. Springer, Cham. https://doi.org/10.1007/978-3-319-44543-4_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-44543-4_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44542-7

  • Online ISBN: 978-3-319-44543-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation