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International Conference on Computational Data and Social Networks

CSoNet 2020: Computational Data and Social Networks pp 499–511Cite as

A Cutting Plane Method for Least Cost Influence Maximization

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12575))

Abstract

We study the least cost influence maximization problem, which has potential applications in social network analysis, as well as in other types of networks. The focus of this paper is on mixed-integer programming (MIP) techniques for the considered problem. The standard arc-based MIP formulation contains a substructure that is a relaxation of the mixed 0-1 knapsack polyhedron. We give a new exponential class of facet-defining inequalities from this substructure and an exact polynomial time separation algorithm for the inequalities. We report preliminary computational results to illustrate the effect of these inequalities.

This material is based on work supported by the AFRL Mathematical Modeling and Optimization Institute.

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References

  1. https://www.python-mip.com/

  2. Ackerman, E., Ben-Zwi, O., Wolfovitz, G.: Combinatorial model and bounds for target set selection. Theor. Comput. Sci. 411(44–46), 4017–4022 (2010)

    Article  MathSciNet  Google Scholar 

  3. Chen, N.: On the approximability of influence in social networks. SIAM J. Discret. Math. 23(3), 1400–1415 (2009)

    Article  MathSciNet  Google Scholar 

  4. Chen, W., Lakshmanan, L.V., Castillo, C.: Information and influence propagation in social networks. Synth. Lect. Data Manag. 5(4), 1–177 (2013)

    Article  Google Scholar 

  5. Demaine, E.D., et al.: How to influence people with partial incentives. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 937–948 (2014)

    Google Scholar 

  6. Fischetti, M., Kahr, M., Leitner, M., Monaci, M., Ruthmair, M.: Least cost influence propagation in (social) networks. Math. Program. 170(1), 293–325 (2018)

    Article  MathSciNet  Google Scholar 

  7. Granovetter, M.: Threshold models of collective behavior. Am. J. Sociol. 83(6), 1420–1443 (1978)

    Article  Google Scholar 

  8. Günneç, D., Raghavan, S., Zhang, R.: A branch-and-cut approach for the least cost influence problem on social networks. Networks 76(1), 84–105 (2020)

    Article  MathSciNet  Google Scholar 

  9. Günneç, D., Raghavan, S., Zhang, R.: Least-cost influence maximization on social networks. INFORMS J. Comput. 32(2), 289–302 (2020)

    MathSciNet  Google Scholar 

  10. Gursoy, F., Günneç, D.: Influence maximization in social networks under deterministic linear threshold model. Knowl.-Based Syst. 161, 111–123 (2018)

    Article  Google Scholar 

  11. Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146 (2003)

    Google Scholar 

  12. Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. Theory Comput. 11(4), 105–147 (2015)

    Article  MathSciNet  Google Scholar 

  13. Marchand, H., Wolsey, L.A.: The 0–1 knapsack problem with a single continuous variable. Math. Program. 85(1), 15–33 (1999)

    Article  MathSciNet  Google Scholar 

  14. Nannicini, G., Sartor, G., Traversi, E., Wolfler Calvo, R.: An exact algorithm for robust influence maximization. Math. Program. 183(1), 419–453 (2020)

    Article  MathSciNet  Google Scholar 

  15. Raghavan, S., Zhang, R.: A branch-and-cut approach for the weighted target set selection problem on social networks. Inf. J. Optim. 1(4), 304–322 (2019)

    MathSciNet  Google Scholar 

  16. Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393(6684), 440–442 (1998)

    Article  Google Scholar 

  17. Wu, H.H., Küçükyavuz, S.: A two-stage stochastic programming approach for influence maximization in social networks. Comput. Optim. Appl. 69(3), 563–595 (2018)

    Article  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. University of Central Florida, Orlando, FL, 32816, USA

    Cheng-Lung Chen & Vladimir Boginski

  2. Air Force Research Laboratory, Eglin AFB, FL, 32542, USA

    Eduardo L. Pasiliao

Authors
  1. Cheng-Lung Chen
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  2. Eduardo L. Pasiliao
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  3. Vladimir Boginski
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Corresponding author

Correspondence to Vladimir Boginski .

Editor information

Editors and Affiliations

  1. University of South Florida, Tampa, FL, USA

    Sriram Chellappan

  2. The University of Texas at San Antonio, San Antonio, TX, USA

    Kim-Kwang Raymond Choo

  3. New Jersey Institute of Technology, Newark, NJ, USA

    NhatHai Phan

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Chen, CL., Pasiliao, E.L., Boginski, V. (2020). A Cutting Plane Method for Least Cost Influence Maximization. In: Chellappan, S., Choo, KK.R., Phan, N. (eds) Computational Data and Social Networks. CSoNet 2020. Lecture Notes in Computer Science(), vol 12575. Springer, Cham. https://doi.org/10.1007/978-3-030-66046-8_41

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  • DOI: https://doi.org/10.1007/978-3-030-66046-8_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-66045-1

  • Online ISBN: 978-3-030-66046-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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