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Community-based diffusion scheme using Markov chain and spectral clustering for mobile social networks

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Abstract

With the increase in the number of mobile devices such as tablets and smart watches, mobile social networks (MSNs) provide great opportunities for people to exchange information. As a result, information diffusion has become a critical issue in the emerging MSNs. In this paper, we address the problem of finding the top-k influential users who can effectively spread information in a network, which is referred to as the diffusion minimization problem. In order to minimize the spreading period, we can utilize the k-center problem, but which has a time complexity of NP-hard. We propose a community-based diffusion scheme using Markov chain and spectral clustering (CDMS) to minimize the spreading time by adopting a community concept based on the geographic regularity of human mobility in the MSNs. We exploit the Markov chain to predict a node’s mobility patterns and cluster the predicted patterns using the spectral graph theory. Finally, we select the top-k influential nodes in each community. Simulations are performed using the NS-2, based on the home-cell community-based mobility model, to show that the proposed scheme results in MSNs. In addition, we demonstrate that CDMS outperforms the noncommunity-based algorithms in terms of the number of nodes and ratio of k influential nodes.

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Acknowledgements

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science, and Technology (2016R1A2B4010142).

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Authors and Affiliations

  1. Department of Computer Science, Yonsei University, Seoul, Korea

    Jegwang Ryu, Jiho Park, Junyeop Lee & Sung-Bong Yang

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  1. Jegwang Ryu
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  2. Jiho Park
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Correspondence to Sung-Bong Yang.

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Ryu, J., Park, J., Lee, J. et al. Community-based diffusion scheme using Markov chain and spectral clustering for mobile social networks. Wireless Netw 25, 875–887 (2019). https://doi.org/10.1007/s11276-017-1599-6

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