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.
['Yaron Singer']
How to win friends and influence people, truthfully: influence maximization mechanisms for social networks