Information diffusion and influence maximization are important and
extensively studied problems in social networks. Various models and algorithms
have been proposed in the literature in the context of the influence
maximization problem. A crucial assumption in all these studies is that the
influence probabilities are known to the social planner. This assumption is
unrealistic since the influence probabilities are usually private information
of the individual agents and strategic agents may not reveal them truthfully.
Moreover, the influence probabilities could vary significantly with the type of
the information flowing in the network and the time at which the information is
propagating in the network. In this paper, we use a mechanism design approach
to elicit influence probabilities truthfully from the agents. We first work
with a simple model, the influencer model, where we assume that each user knows
the level of influence she has on her neighbors but this is private
information. In the second model, the influencer-influencee model, which is
more realistic, we determine influence probabilities by combining the
probability values reported by the influencers and influencees. In the context
of the first model, we present how VCG (Vickrey-Clarke-Groves) mechanisms could
be used for truthfully eliciting the influence probabilities. Our main
contribution is to design a scoring rule based mechanism in the context of the
influencer-influencee model. In particular, we show the incentive compatibility
of the mechanisms when the scoring rules are proper and propose a reverse
weighted scoring rule based mechanism as an appropriate mechanism to use. We
also discuss briefly the implementation of such a mechanism in viral marketing
applications
