Advertisement

Wireless Networks

, Volume 25, Issue 2, pp 875–887 | Cite as

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

  • Jegwang Ryu
  • Jiho Park
  • Junyeop Lee
  • Sung-Bong YangEmail author
Article
  • 78 Downloads

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.

Keywords

Mobile social networks Information diffusion Markov chain Spectral clustering 

Notes

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).

References

  1. 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).Google Scholar
  2. 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).Google Scholar
  3. 3.
    Nguyen, H. A., & Silvia, G. (2009). Routing in opportunistic networks. International Journal of Ambient Computing and Intelligence, 1(3), 19–38.CrossRefGoogle Scholar
  4. 4.
    Conti, M., Giordano, S., May, M., & Passarella, A. (2010). From opportunistic networks to opportunistic computing. IEEE Communications Magazine, 48(9), 126–139.CrossRefGoogle Scholar
  5. 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).Google Scholar
  6. 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).Google Scholar
  7. 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).Google Scholar
  8. 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.MathSciNetCrossRefzbMATHGoogle Scholar
  9. 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.MathSciNetCrossRefzbMATHGoogle Scholar
  10. 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).Google Scholar
  11. 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.MathSciNetCrossRefzbMATHGoogle Scholar
  12. 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.CrossRefGoogle Scholar
  13. 13.
    Von Luxburg, U. (2007). A tutorial on spectral clustering. Statistics and computing, 17(4), 395–416.MathSciNetCrossRefGoogle Scholar
  14. 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.Google Scholar
  15. 15.
    Network Simulator-2. (2014). http://www.isi.edu/nsnam/ns/.
  16. 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.CrossRefGoogle Scholar
  17. 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.CrossRefGoogle Scholar
  18. 18.
    Lambiotte, R., & Panzarasa, P. (2009). Communities, knowledge creation, and information diffusion. Journal of Informetrics, 3(3), 180–190.CrossRefGoogle Scholar
  19. 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).Google Scholar
  20. 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).Google Scholar
  21. 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).Google Scholar
  22. 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).Google Scholar
  23. 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).Google Scholar
  24. 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).Google Scholar
  25. 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.CrossRefGoogle Scholar
  26. 26.
  27. 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).Google Scholar
  28. 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).Google Scholar
  29. 29.
    Yu, Z., Yu, Z., & Chen, Y. (2016). Multi-hop mobility prediction. Mobile Networks and Applications, 21(2), 367–374.CrossRefGoogle Scholar
  30. 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.MathSciNetCrossRefzbMATHGoogle Scholar
  31. 31.
    Fiedler, M. (1973). Algebraic connectivity of graphs. Czechoslovak Mathematical Journal, 23(2), 298–305.MathSciNetzbMATHGoogle Scholar
  32. 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.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Jegwang Ryu
    • 1
  • Jiho Park
    • 1
  • Junyeop Lee
    • 1
  • Sung-Bong Yang
    • 1
    Email author
  1. 1.Department of Computer ScienceYonsei UniversitySeoulKorea

Personalised recommendations