Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Approximating the value of two power proof systems, with applications to MAX 2SAT and MAX DICUT
ISTCS '95 Proceedings of the 3rd Israel Symposium on the Theory of Computing Systems (ISTCS'95)
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Optimal marketing strategies over social networks
Proceedings of the 17th international conference on World Wide Web
Beating the Random Ordering is Hard: Inapproximability of Maximum Acyclic Subgraph
FOCS '08 Proceedings of the 2008 49th Annual IEEE Symposium on Foundations of Computer Science
Pricing Strategies for Viral Marketing on Social Networks
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
Optimal pricing in the presence of local network effects
WINE'10 Proceedings of the 6th international conference on Internet and network economics
Optimal iterative pricing over social networks
WINE'10 Proceedings of the 6th international conference on Internet and network economics
Equilibrium pricing with positive externalities
WINE'10 Proceedings of the 6th international conference on Internet and network economics
Optimal auctions with positive network externalities
Proceedings of the 12th ACM conference on Electronic commerce
Externalities among advertisers in sponsored search
SAGT'11 Proceedings of the 4th international conference on Algorithmic game theory
Optimal pricing in social networks with incomplete information
WINE'11 Proceedings of the 7th international conference on Internet and Network Economics
A Tight Linear Time (1/2)-Approximation for Unconstrained Submodular Maximization
FOCS '12 Proceedings of the 2012 IEEE 53rd Annual Symposium on Foundations of Computer Science
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We consider the marketing model of (Hartline, Mirrokni, Sundararajan, WWW '08) for selling a digital product in a social network under positive externalities. The seller seeks for a marketing strategy, namely an ordering in which he approaches the buyers and the prices offered to them, that maximizes her revenue. We restrict our attention to the Uniform Additive Model of externalities, and mostly focus on Influence-and-Exploit (IE) marketing strategies. We show that in undirected social networks, revenue maximization is NP -hard not only when we search for a general optimal marketing strategy, but also when we search for the best IE strategy. Rather surprisingly, we observe that allowing IE strategies to offer prices smaller than the myopic price in the exploit step leads to a significant improvement on their performance. Thus, we show that the best IE strategy approximates the maximum revenue within a factor of 0.911 for undirected and of roughly 0.553 for directed networks. Utilizing a connection between good IE strategies and large cuts in the underlying social network, we obtain polynomial-time algorithms that approximate the revenue of the best IE strategy within a factor of roughly 0.9. Hence, we significantly improve on the best known approximation ratio for the maximum revenue to 0.8229 for undirected and to 0.5011 for directed networks (from 2/3 and 1/3, respectively).