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Threshold models for competitive influence in social networks

Published: 13 December 2010 Publication History

Abstract

The problem of influence maximization deals with choosing the optimal set of nodes in a social network so as to maximize the resulting spread of a technology (opinion, product-ownership, etc.), given a model of diffusion of influence in a network. A natural extension is a competitive setting, in which the goal is to maximize the spread of our technology in the presence of one or more competitors.
We suggest several natural extensions to the well-studied linearthreshold model, showing that the original greedy approach cannot be used.
Furthermore, we show that for a broad family of competitive influence models, it is NP-hard to achieve an approximation that is better than a square root of the optimal solution; the same proof can also be applied to give a negative result for a conjecture in [2] about a general cascade model for competitive diffusion.
Finally, we suggest a natural model that is amenable to the greedy approach.

References

[1]
Bharathi, S., Kempe, D., Salek, M.: Competitive influence maximization in social networks. In:WINE 2007. LNCS, vol. 4858, pp. 306-311. Springer, Heidelberg (2007)
[2]
Carnes, T., Nagarajan, C., Wild, S.M., van Zuylen, A.: Maximizing influence in a competitive social network: a follower's perspective. In: ICEC 2007: Proceedings of the Ninth International Conference on Electronic Commerce, pp. 351-360. ACM, New York (2007)
[3]
Domingos, P., Richardson, M.: Mining the network value of customers. In: KDD 2001: Proceedings of the seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 57-66. ACM Press, New York (2001)
[4]
Nemhauser, M.F.G., Wolsey, L.: An analysis of the approximations for maximizing submodular set functions. Mathematical Programming, 14, 265-294 (1978)
[5]
Brown, P.R.J.: Social ties and word-of-mouth referral behavior. Journal of Consumer Research 14(3), 350-362 (1987)
[6]
Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: KDD 2003: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137-146. ACM, New York (2003)
[7]
Kostka, J., Oswald, Y.A., Wattenhofer, R.: Word of mouth: Rumor dissemination in social networks. In: Shvartsman, A.A., Felber, P. (eds.) SIROCCO 2008. LNCS, vol. 5058, pp. 185-196. Springer, Heidelberg (2008)
[8]
Mossel, E., Roch, S.: Submodularity of influence in social networks: From local to global. SIAM J. Comput. 39(6), 2176-2188 (2010)

Cited By

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  • (2021)Collective influence maximization for multiple competing products with an awareness-to-influence modelProceedings of the VLDB Endowment10.14778/3450980.345098114:7(1124-1136)Online publication date: 1-Mar-2021
  • (2021)Mitigating Negative Influence Diffusion is HardProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482451(332-341)Online publication date: 26-Oct-2021
  • (2020)Viral vs. EffectiveProceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3398761.3398897(1169-1177)Online publication date: 5-May-2020
  • Show More Cited By

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Published In

cover image Guide Proceedings
WINE'10: Proceedings of the 6th international conference on Internet and network economics
December 2010
578 pages
ISBN:3642175716
  • Editor:
  • Amin Saberi

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 13 December 2010

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View all
  • (2021)Collective influence maximization for multiple competing products with an awareness-to-influence modelProceedings of the VLDB Endowment10.14778/3450980.345098114:7(1124-1136)Online publication date: 1-Mar-2021
  • (2021)Mitigating Negative Influence Diffusion is HardProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482451(332-341)Online publication date: 26-Oct-2021
  • (2020)Viral vs. EffectiveProceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3398761.3398897(1169-1177)Online publication date: 5-May-2020
  • (2020)Addressing competitive influence maximization on unknown social network with deep reinforcement learningProceedings of the 12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.1109/ASONAM49781.2020.9381471(196-203)Online publication date: 7-Dec-2020
  • (2019)Influence maximisation in social networksInternational Journal of Computational Science and Engineering10.5555/3319216.331921718:2(103-117)Online publication date: 1-Jan-2019
  • (2019)Maximizing the Spread of an Opinion when Tertium Datur EstProceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3306127.3331823(1207-1215)Online publication date: 8-May-2019
  • (2019)Combating fake newsProceedings of the VLDB Endowment10.14778/3352063.335211712:12(1990-1993)Online publication date: 1-Aug-2019
  • (2019)A Distance Measure for the Analysis of Polar Opinion Dynamics in Social NetworksACM Transactions on Knowledge Discovery from Data10.1145/333216813:4(1-34)Online publication date: 8-Aug-2019
  • (2019)Budget-Feasible Mechanism Design for Non-Monotone Submodular ObjectivesProceedings of the 2019 ACM Conference on Economics and Computation10.1145/3328526.3329622(901-919)Online publication date: 17-Jun-2019
  • (2019)Debunking Rumors in Social NetworksProceedings of the 10th ACM Conference on Web Science10.1145/3292522.3326025(323-331)Online publication date: 26-Jun-2019
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