The budgeted maximum coverage problem
Information Processing Letters
Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
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
Fast discovery of connection subgraphs
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
Beyond VCG: Frugality of Truthful Mechanisms
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
The dynamics of viral marketing
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
On the submodularity of influence in social networks
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
On the approximability of influence in social networks
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
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
Learning influence probabilities in social networks
Proceedings of the third ACM international conference on Web search and data mining
How much is your personal recommendation worth?
Proceedings of the 19th international conference on World wide web
Walking in facebook: a case study of unbiased sampling of OSNs
INFOCOM'10 Proceedings of the 29th conference on Information communications
Inferring networks of diffusion and influence
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
FOCS '10 Proceedings of the 2010 IEEE 51st Annual Symposium on Foundations of Computer Science
A note on maximizing the spread of influence in social networks
Information Processing Letters
Everyone's an influencer: quantifying influence on twitter
Proceedings of the fourth ACM international conference on Web search and data mining
Optimal auctions with positive network externalities
Proceedings of the 12th ACM conference on Electronic commerce
Mechanisms for complement-free procurement
Proceedings of the 12th ACM conference on Electronic commerce
Sparsification of influence networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Finding red balloons with split contracts: robustness to individuals' selfishness
STOC '12 Proceedings of the forty-fourth annual ACM symposium on Theory of computing
Simpler sybil-proof mechanisms for multi-level marketing
Proceedings of the 13th ACM Conference on Electronic Commerce
Strategyproof mechanisms for competitive influence in networks
Proceedings of the 22nd international conference on World Wide Web
Pricing mechanisms for crowdsourcing markets
Proceedings of the 22nd international conference on World Wide Web
Truthful incentives in crowdsourcing tasks using regret minimization mechanisms
Proceedings of the 22nd international conference on World Wide Web
Modelling growth of urban crowd-sourced information
Proceedings of the 7th ACM international conference on Web search and data mining
Proceedings of the 19th international conference on Intelligent User Interfaces
How to influence people with partial incentives
Proceedings of the 23rd international conference on World wide web
Weighted graph-based methods for identifying the most influential actors in trust social networks
International Journal of Networking and Virtual Organisations
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Throughout the past decade there has been extensive research on algorithmic and data mining techniques for solving the problem of influence maximization in social networks: if one can incentivize a subset of individuals to become early adopters of a new technology, which subset should be selected so that the word-of-mouth effect in the social network is maximized? Despite the progress in modeling and techniques, the incomplete information aspect of the problem has been largely overlooked. While data can often provide the network structure and influence patterns may be observable, the inherent cost individuals have to become early adopters is difficult to extract. In this paper we introduce mechanisms that elicit individuals' costs while providing desirable approximation guarantees in some of the most well-studied models of social network influence. We follow the mechanism design framework which advocates for allocation and payment schemes that incentivize individuals to report their true information. We also performed experiments using the Mechanical Turk platform and social network data to provide evidence of the framework's effectiveness in practice.