Target specific influence maximization: An approach to maximize adoption in labeled social networks

Abstract

Influence maximization deals with finding a small set of nodes, called seed set, to be initially influenced such that they will eventually spread the influence to maximum number of users in the social network. This paper deals with a specialization of the basic problem called labeled influence maximization that identifies seeds that will maximize the influence spread among a specific set of target users identified by their attribute values. In a social setting, a large difference exists between awareness and adoption of an idea/product. This notion fits well in case of labeled influence maximization where any user can become 'aware' about a product whereas only specific users 'adopt' the product. This work considers the problem of labeled influence maximization by incorporating the difference between awareness and adoption. Due to the inherent difference in nature, characteristics, and interests of every user, the number of users who adopt a product varies depending on the type of users in the network and the suitability of the product being marketed. Most of the existing diffusion models do not take this into account. This paper proposes a target adoption model that accounts for both awareness and adoption spread in the network, and a heuristic based discounting approach to find the seed set. The proposed approach is evaluated on different datasets and found to outperform the existing heuristics and discounting approaches. The approach causes maximum adoption in the given social network. � 2017 IEEE.