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Communication Dans Un Congrès Année : 2017

Evaluating Seed Selection for Information Diffusion in Mobile Social Networks

Résumé

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 seed-nodes (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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Dates et versions

hal-03109249 , version 1 (20-01-2021)

Identifiants

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Farouk Mezghani, Manel Mezghani, Ahmad Kaouk, André-Luc Beylot, Florence Sèdes. Evaluating Seed Selection for Information Diffusion in Mobile Social Networks. IEEE Wireless Communications and Networking Conference (WCNC 2017), Mar 2017, San Francisco, CA, United States. pp.1-6, ⟨10.1109/WCNC.2017.7925787⟩. ⟨hal-03109249⟩
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