Skip to main content
SpringerLink
Log in
Book cover

International Colloquium on Structural Information and Communication Complexity

SIROCCO 2015: Structural Information and Communication Complexity pp 119–134Cite as

Optimizing Spread of Influence in Social Networks via Partial Incentives

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9439))

Abstract

A widely studied process of influence diffusion in social networks posits that the dynamics of influence diffusion evolves as follows: Given a graph G = (V,E), representing the network, initially only the members of a given S ⊆ V are influenced; subsequently, at each round, the set of influenced nodes is augmented by all the nodes in the network that have a sufficiently large number of already influenced neighbors. The general problem is to find a small initial set of nodes that influences the whole network. In this paper we extend the previously described basic model in the following ways: firstly, we assume that there are non negative values c(v) associated to each node v ∈ V, measuring how much it costs to initially influence node v, and the algorithmic problem is to find a set of nodes of minimum total cost that influences the whole network; successively, we study the consequences of giving incentives to member of the networks, and we quantify how this affects (i.e., reduces) the total costs of starting an influence diffusion process that influence the whole network. For the two above problems we provide both hardness results and algorithms. We also experimentally validate our algorithms via extensive simulations on real life networks.

This is a preview of subscription content, 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 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

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

    Article  MathSciNet  MATH  Google Scholar 

  2. Bakshy, E., Hofman, J.M., Mason, W.A., Watts, D.J.: Everyone’s an influencer: quantifying influence on twitter. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining (WSDM 2011), pp. 65–74 (2011)

    Google Scholar 

  3. Bazgan, C., Chopin, M., Nichterlein, A., Sikora, F.: Parametrized Approximability of Maximizing the Spread of Influence in Networks. In: Du, D.-Z., Zhang, G. (eds.) COCOON 2013. LNCS, vol. 7936, pp. 543–554. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  5. Centeno, C.C., et al.: Irreversible conversion of graphs. Theoretical Computer Science 412(29), 3693–3700 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  6. Chopin, M., Nichterlein, A., Niedermeier, R., Weller, M.: Constant Thresholds Can Make Target Set Selection Tractable. In: Even, G., Rawitz, D. (eds.) MedAlg 2012. LNCS, vol. 7659, pp. 120–133. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  7. Coja-Oghlan, A., Feige, U., Krivelevich, M., Reichman, D.: Contagious Sets in Expanders. In: Proceedings of SODA 2015 (1987)

    Google Scholar 

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

    Google Scholar 

  9. Chen, W., Wang, Y., Yang, S.: Efficient Influence Maximization in Social Networks. In: Proc. 15th ACM SIGKDD Intl. Conf. on Know. Dis. and Data Min. (2009)

    Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  11. Chiang, C.-Y., Huang, L.-H., Yeh, H.-G.: Target Set Selection Problem for Honeycomb Networks. SIAM J. Discrete Math. 27(1), 310–328 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  12. Chiang, C.-Y., Huang, L.-H., Li, B.-J., Wu, J., Yeh, H.-G.: Some results on the target set selection problem. Journal of Comb. Opt. 25(4), 702–715 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  13. Cicalese, F., Cordasco, G., Gargano, L., Milanič, M., Peters, J.G., Vaccaro, U.: Spread of Influence in Weighted Networks under Time and Budget Constraints. Theoretical Computer Science 586, 40–58 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  14. Cicalese, F., Cordasco, G., Gargano, L., Milanič, M., Vaccaro, U.: Latency-Bounded Target Set Selection in Social Networks. Theoretical Computer Science 535, 1–15 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  15. Christakis, N.A., Fowler, J.H.: The collective dynamics of smoking in a large social network. N. Engl. J. Med., 2249–2258 (2008)

    Google Scholar 

  16. Demaine, E.D., Hajiaghayi, M.T., Mahini, H., Malec, D.L., Raghavan, S., Sawant, A., Zadimoghadam, M.: How to influence people with partial incentives. In: Proc. of WWW 2014, pp. 937–948 (2014)

    Google Scholar 

  17. Domingos, P., Richardson, M.: Mining the network value of customers. In: Proc. of 7th ACM SIGKDD Int. Conf. on Know. Disc. and Data Min., pp. 57–66 (2001)

    Google Scholar 

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

    Google Scholar 

  19. Flocchini, P., Královic, R., Ruzicka, P., Roncato, A., Santoro, N.: On time versus size for monotone dynamic monopolies in regular topologies. J. Discrete Algorithms 1, 129–150 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  20. Gargano, L., Hell, P., Peters, J., Vaccaro, U.: Influence Diffusion in Social Networks under Time Window Constraints. In: Moscibroda, T., Rescigno, A.A. (eds.) SIROCCO 2013. LNCS, vol. 8179, pp. 141–152. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  21. Granovetter, M.: Thresholds Models of Collective Behaviors. American Journal of Sociology 83(6), 1420–1443 (1978)

    Article  Google Scholar 

  22. Kempe, D., Kleinberg, J.M., Tardos, E.: Maximizing the spread of influence through a social network. In: Proc. of 9th ACM SIGKDD Int. Conf. on Know., Disc. and Data Min., pp. 137–146 (2003)

    Google Scholar 

  23. Kempe, D., Kleinberg, J.M., Tardos, É.: Influential vertices in a Diffusion Model for Social Networks. In: Caires, L., Italiano, G.F., Monteiro, L., Palamidessi, C., Yung, M. (eds.) ICALP 2005. LNCS, vol. 3580, pp. 1127–1138. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  24. Leskovic, H., Adamic, L.A., Huberman, B.A.: The dynamic of viral marketing. ACM Transactions on the WEB 1 (2007)

    Google Scholar 

  25. Leskovec, J., Krevl, A.: SNAP Datasets: Stanford Large Network Dataset Collection (2015), http://snap.stanford.edu/data

  26. Newman, M.: Network data (2015), http://www-personal.umich.edu/~mejn/netdata/

  27. Nichterlein, A., Niedermeier, R., Uhlmann, J., Weller, M.: On Tractable Cases of Target Set Selection. Social Network Analysis and Mining, 1–24 (2012)

    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. Zafarani, R., Liu, H.: Social Computing Data Repository at ASU, http://socialcomputing.asu.edu

  30. Zaker, M.: On dynamic monopolies of graphs with general thresholds. Discrete Mathematics 312(6), 1136–1143 (2012)

    Article  MathSciNet  MATH  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

Editor information

Editors and Affiliations

  1. Dept of Mathematics and Comp Science, Paderborn University, Paderborn, Nordrhein-Westfalen, Germany

    Christian Scheideler

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Cordasco, G., Gargano, L., Rescigno, A.A., Vaccaro, U. (2015). Optimizing Spread of Influence in Social Networks via Partial Incentives. In: Scheideler, C. (eds) Structural Information and Communication Complexity. SIROCCO 2015. Lecture Notes in Computer Science(), vol 9439. Springer, Cham. https://doi.org/10.1007/978-3-319-25258-2_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-25258-2_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25257-5

  • Online ISBN: 978-3-319-25258-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation