Mechanisms for Maximizing Influence in Social Networks

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 convince a subset of individuals to influence their friends to adopt a new product or technology, which subset should be selected so that the spread of influence 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 for influencing friends is difficult to extract. In this paper we introduce mechanisms that extract individuals’ costs while providing constant factor guarantees in some of the well studied models of influence in social networks. 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 on the mechanical turk platform, which together with social network data provides experimental evidence that suggests that beyond provable guarantees the framework allows for mechanisms that work well in practice.