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Information diffusion and influence maximization are important andextensively studied problems in social networks. Various models and algorithmshave been proposed in the literature in the context of the influencemaximization problem. A crucial assumption in all these studies is that theinfluence probabilities are known to the social planner. This assumption isunrealistic since the influence probabilities are usually private informationof the individual agents and strategic agents may not reveal them truthfully.Moreover, the influence probabilities could vary significantly with the type ofthe information flowing in the network and the time at which the information ispropagating in the network. In this paper, we use a mechanism design approachto elicit influence probabilities truthfully from the agents. We first workwith a simple model, the influencer model, where we assume that each user knowsthe level of influence she has on her neighbors but this is privateinformation. In the second model, the influencer-influencee model, which ismore realistic, we determine influence probabilities by combining theprobability values reported by the influencers and influencees. In the contextof the first model, we present how VCG (Vickrey-Clarke-Groves) mechanisms couldbe used for truthfully eliciting the influence probabilities. Our maincontribution is to design a scoring rule based mechanism in the context of theinfluencer-influencee model. In particular, we show the incentive compatibilityof the mechanisms when the scoring rules are proper and propose a reverseweighted scoring rule based mechanism as an appropriate mechanism to use. Wealso discuss briefly the implementation of such a mechanism in viral marketingapplications.

Incentive Compatible Influence Maximization in Social Networks and Application to Viral Marketing