Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
The dynamics of viral marketing
ACM Transactions on the Web (TWEB)
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Lottery trees: motivational deployment of networked systems
Proceedings of the 2007 conference on Applications, technologies, architectures, and protocols for computer communications
Optimal marketing strategies over social networks
Proceedings of the 17th international conference on World Wide Web
Pricing Strategies for Viral Marketing on Social Networks
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
Mechanisms for multi-level marketing
Proceedings of the 12th ACM conference on Electronic commerce
Proceedings of the fifth ACM international conference on Web search and data mining
Proceedings of the 13th ACM Conference on Electronic Commerce
Mechanism design for time critical and cost critical task execution via crowdsourcing
WINE'12 Proceedings of the 8th international conference on Internet and Network Economics
Sybil-proof mechanisms in query incentive networks
Proceedings of the fourteenth ACM conference on Electronic commerce
Fair and resilient incentive tree mechanisms
Proceedings of the 2013 ACM symposium on Principles of distributed computing
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Multi-level marketing refers to a marketing approach in which buyers are encouraged to take an active role in promoting the product. This is done by offering them a reward for each successful referral of the product to other prospective buyers. To encourage potential customers to buy early and to give referrals to influential people, these mechanisms also reward indirect referrals --- a direct referral linked to the buyer through other direct referrals. Doing so can make the referral/reward system vulnerable to sybil attacks --- where profit maximizers create several replicas in order to maximize their rewards. In this paper we propose a family of mechanisms for which sybil attacks are not profitable. We do this by modifying any mechanism that satisfies certain natural properties of sensible reward mechanisms to obtain one that is invulnerable to sybil attacks by profit maximizers while preserving its natural properties. Our modified mechanisms are also collusion proof. Finally, we give a concrete example of a natural mechanism that is sybil proof and simple to implement.