Influence propagation in adversarial setting: How to defeat competition with least amount of investment

Shahrzad Shirazipourazad, Brian Bogard, Harsh Vachhani, Arunabha Sen, Paul Horn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

30 Scopus citations

Abstract

It has been observed that individuals' decisions to adopt a product or innovation are often influenced by the recommendations of their friends and acquaintances. Motivated by this observation, the last few years have seen a number of studies on influence maximization in social networks. The primary goal of these studies is identification of k most influential nodes in a network. A major limitation of these studies is that they focus on a non-adversarial environment, where only one player is engaged in influencing the nodes. However, in a realistic scenario multiple players attempt to influence the nodes in a competitive fashion. The proposed model considers a competitive environment where a node that has not yet adopted an innovation, can adopt only one of the several competing innovations and once it adopts an innovation, it does not switch. The paper studies the scenario where the first player has already chosen a set of k nodes and the second player, with the knowledge of the choice of the first, attempts to identify a smallest set of nodes (excluding the ones already chosen by the first) so that when the influence propagation process ends, the number of nodes influenced by the second player is larger than the number of nodes influenced by the first. The paper studies two propagation models and shows that in both the models, the identification of the smallest set of nodes to defeat the adversary is NP-Hard. It provides an approximation algorithm and proves that the performance bound is tight. It also presents the results of extensive experimentation using the collaboration network data. Experimental results show that the second player can easily defeat the first with this algorithm, if the first utilizes the node degree or closeness centrality based algorithms for the selection of influential nodes. The proposed algorithm also provides better performance if the second player utilizes it instead of the greedy algorithm to maximize its influence.

Original languageEnglish (US)
Title of host publicationCIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management
Pages585-594
Number of pages10
DOIs
StatePublished - Dec 19 2012
Event21st ACM International Conference on Information and Knowledge Management, CIKM 2012 - Maui, HI, United States
Duration: Oct 29 2012Nov 2 2012

Publication series

NameACM International Conference Proceeding Series

Other

Other21st ACM International Conference on Information and Knowledge Management, CIKM 2012
CountryUnited States
CityMaui, HI
Period10/29/1211/2/12

Keywords

  • adversarial environment
  • influence maximization
  • social networks

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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  • Cite this

    Shirazipourazad, S., Bogard, B., Vachhani, H., Sen, A., & Horn, P. (2012). Influence propagation in adversarial setting: How to defeat competition with least amount of investment. In CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management (pp. 585-594). (ACM International Conference Proceeding Series). https://doi.org/10.1145/2396761.2396837