Evaluating Seed Selection for Information Diffusion in Mobile Social Networks

Abstract

The integration of social networks with mobile communication has led to the rise of a new paradigm, the mobile social network (MSN). Recently, MSN has emerged as a new hot spot of research attracting much interest from both academia and industrial sectors. For instance, MSN opens new horizon for information diffusion-based applications such as viral marketing.Thus, it is a fundamental issue to select an efficient subset of seednodes (i.e. initial sources) in a MSN such that targeting them initially will maximize the information diffusion to interested nodes. This paper studies the problem of identifying the best seeds through whom the information can be diffused in the network in order to maximize the content utility (i.e. a quantitative metric that determines how satisfied are the users). A multi-layer model that combines the social relationships and the mobile network in order to design an efficient information diffusion is proposed. Based on this multi-layer model, different seed selection approaches are proposed for information diffusion environment (e.g. mobile advertising) where users have heterogeneous interests for the different information generated in the network. Simulation results show the effectiveness of multi-layer based seed selection approaches comparing to a classical approach.

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“…In [30], Liu et al proposed a multiple source selection method to find the optimal number of initial sources. In addition, a multilayer-based seed selection approach is designed in [31] to maximally satisfy users' interest. The results show that multilayer-based seed scheme allows to maximize content utility.…”