Title: Maximizing Influence in a Competitive Social Network: A Follower's Perspective
Authors: Tim Carnes, Chandrashekhar Nagarajan, Stefan Wild, Anke van Zuylen
Abstract: We consider the problem faced by a company that wants to use viral marketing to introduce a new product into a market where a competing product is already being introduced. We assume that consumers will use only one of the two products and will influence their friends in their decision of which product to use. We propose two models for the spread of influence of competing technologies through a social network and consider the influence maximization problem from the follower's perspective. In particular we assume the follower has a fixed budget available that can be used to target a subset of consumers and show that, although it is NP-hard to select the most influential subset to target, it is possible to give an efficient algorithm that is within 63% of optimal. Our computational experiments show that by using knowledge of the social network and the set of consumers targeted by the competitor, the follower may in fact capture a majority of the market by targeting a relatively small set of the right consumers.
Keywords: Approximation Algorithms, Social Networks, Viral Marketing, Network Analysis, Targeted Marketing
Thanks: Research supported by NSF grants CCR-0635121, DMI-0500263, and CCF-0514628, and by a DOE Computational Science Graduate Fellowship under grant number DE-FG02-97ER25308.
Status: Appeared in the proceedings of the Ninth International Conference on Electronic Commerce, August 2007.
This version replaces School of Operations Research and Information Engineering Technical Report ORIE-1459, December 2006
Link: [PDF through ACM]
    author = {Tim Carnes and Chandrashekhar Nagarajan and Stefan M. Wild and Anke van Zuylen},
    title = {Maximizing influence in a competitive social network: a follower's perspective},
    booktitle = {ICEC '07: Proceedings of the ninth international conference on Electronic commerce},
    year = {2007},
    isbn = {978-1-59593-700-1},
    pages = {351--360},
    location = {Minneapolis, MN, USA},
    doi = {http://doi.acm.org/10.1145/1282100.1282167},
    publisher = {ACM},
    address = {New York, NY, USA},
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