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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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References

  1. Ma, H., Yang, H., Lyu, M. R., & King, I. (2008). Mining social networks using heat diffusion processes for marketing candidates selection. In Proceedings of the 17th ACM conference on Information and knowledge management (pp. 233–242).

  2. Richardson, M., & Domingos, P. (2002). Mining knowledge-sharing sites for viral marketing. In Proceedings of the 8th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 61–70).

  3. Nguyen, H. A., & Silvia, G. (2009). Routing in opportunistic networks. International Journal of Ambient Computing and Intelligence, 1(3), 19–38.

    Article  Google Scholar 

  4. Conti, M., Giordano, S., May, M., & Passarella, A. (2010). From opportunistic networks to opportunistic computing. IEEE Communications Magazine, 48(9), 126–139.

    Article  Google Scholar 

  5. Lu, Z., Wen, Y., & Cao, G. (2014). Information diffusion in mobile social networks: The speed perspective. In Proceedings of IEEE INFOCOM (pp. 1932–1940).

  6. Chen, X., & Xiong, K. (2015). Dynamic social feature-based diffusion in mobile social networks. In Proceedings of IEEE/CIC International Conference on Communications in China (ICCC) (pp. 1–6).

  7. Myers, S. A., Zhu, C., & Leskovec, J. (2012). Information diffusion and external influence in networks. In Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 33–41).

  8. Panigrahy, R., & Vishwanathan, S. (1998). An O (log*n) approximation algorithm for the asymmetric p-center problem. Journal of Algorithms, 27(2), 259–268.

    Article  MathSciNet  MATH  Google Scholar 

  9. Girvan, M., & Newman, M. E. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences, 99(12), 7821–7826.

    Article  MathSciNet  MATH  Google Scholar 

  10. Hsu, W. J., Spyropoulos, T., Psounis, K., & Helmy, A. (2007). Modeling time-variant user mobility in wireless mobile networks. In Proceedings of IEEE INFOCOM (pp. 758–766).

  11. van Gennip, Y., Hunter, B., Ahn, R., Elliott, P., Luh, K., Halvorson, M., et al. (2013). Community detection using spectral clustering on sparse geosocial data. SIAM Journal on Applied Mathematics., 73(1), 67–83.

    Article  MathSciNet  MATH  Google Scholar 

  12. Zhang, S., Wang, R. S., & Zhang, X. S. (2007). Identification of overlapping community structure in complex networks using fuzzy c-means clustering. Statistical Mechanics and its Applications, 374(1), 483–490.

    Article  Google Scholar 

  13. Von Luxburg, U. (2007). A tutorial on spectral clustering. Statistics and computing, 17(4), 395–416.

    Article  MathSciNet  Google Scholar 

  14. Ng, A. Y., Jordan, M. I., & Weiss, Y. (2001). On spectral clustering: Analysis and an algorithm. In Proceedings of Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press.

  15. Network Simulator-2. (2014). http://www.isi.edu/nsnam/ns/.

  16. Christakis, N. A., & Fowler, J. H. (2007). The spread of obesity in a large social network over 32 years. New England Journal of Medicine, 357(4), 370–379.

    Article  Google Scholar 

  17. Centola, D., Eguíluz, V. M., & Macy, M. W. (2007). Cascade dynamics of complex propagation. Physica A: Statistical Mechanics and its Applications, 374(1), 449–456.

    Article  Google Scholar 

  18. Lambiotte, R., & Panzarasa, P. (2009). Communities, knowledge creation, and information diffusion. Journal of Informetrics, 3(3), 180–190.

    Article  Google Scholar 

  19. Sun, X., Lu, Z., Zhang, X., Salathé, M., & Cao, G. (2015). Targeted vaccination based on a wireless sensor system. In Proceedings of Pervasive Computing and communications workshops (pp. 215–220).

  20. Bakshy, E., Rosenn, I., Marlow, C., & Adamic, L. (2012). The role of social networks in information diffusion. In Proceedings of the 21th international conference on World Wide Web (pp. 519–528).

  21. Romero, D. M., Meeder, B., & Kleinberg, J. (2011). Differences in the mechanics of information diffusion across topics: Idioms, political hashtags, and complex contagion on twitter. In Proceedings of the 20th international conference on World wide web (pp. 695–704).

  22. Domingos, P., & Richardson, M. (2001). Mining the network value of customers. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 57–66).

  23. Kempe, D., Kleinberg, J., & Tardos, É. (2003). Maximizing the spread of influence through a social network. In Proceedings of the 9th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 137–146).

  24. Wang, Y., Cong, G., Song, G., & Xie, K. (2010). Community-based greedy algorithm for mining top-k influential nodes in mobile social networks. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1039–1048).

  25. Han, B., Hui, P., Kumar, V. A., Marathe, M. V., Shao, J., & Srinivasan, A. (2012). Mobile data offloading through opportunistic communications and social participation. IEEE Transactions on Mobile Computing, 11(5), 821–834.

    Article  Google Scholar 

  26. Markov chain. (2016). https://en.wikipedia.org/wiki/Markov_chain.

  27. Soelistijanto, B., & Howarth, M. (2012). Traffic distribution and network capacity analysis in social opportunistic networks. In Proceedings of the 8th IEEE international conference on the wireless and mobile computing, networking and communications (WiMob) (pp. 823–830).

  28. Lee, J. K., & Hou, J. C. (2006). Modeling steady-state and transient behaviors of user mobility: Formulation, analysis, and application. In Proceedings of the 7th ACM international symposium on mobile ad hoc networking and computing (pp. 85–96).

  29. Yu, Z., Yu, Z., & Chen, Y. (2016). Multi-hop mobility prediction. Mobile Networks and Applications, 21(2), 367–374.

    Article  Google Scholar 

  30. Donath, W. E., & Hoffman, A. J. (1973). Lower bounds for the partitioning of graphs. IBM Journal of Research and Development, 17(5), 420–425.

    Article  MathSciNet  MATH  Google Scholar 

  31. Fiedler, M. (1973). Algebraic connectivity of graphs. Czechoslovak Mathematical Journal, 23(2), 298–305.

    MathSciNet  MATH  Google Scholar 

  32. Boldrini, C., & Passarella, A. (2010). HCMM: Modelling spatial and temporal properties of human mobility driven by users’ social relationships. Computer Communications, 33(9), 1056–1074.

    Article  Google Scholar 

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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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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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