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High-performance social networking: microblog community detection based on efficient interactive characteristic clustering

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Abstract

With the development of microblog networks and the popularity of the friend circle, more and more users are linked together to form communities. The microblog community detection is not only the separation of following relationships. The interactive characteristic between users should also be considered. Therefore, in the article the maximum likelihood estimation is used to extract the interactive characteristics clustering. The Link Optimization Comm (LOC) algorithm based on density clustering is proposed to improve the performance the Link Comm (LC) algorithm. By integrating the interactive characteristic clustering into LOC algorithm, the high-performance IC-LOC method to detect potential communities is proposed. Then the complexity in clustering is analyzed. Simulation experiments show that LOC algorithm is better than LC algorithm in time complexity and normalized mutual information evaluation. Compared with LC algorithm in Sina microblog data sets, the IC-LOC method also achieves better performance of community detection. Moreover, the proposed method can effectively detect interactive and potential communities.

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References

  1. Huang, L., Wang, S., Hsu, C.H., Zhang, J., Yang, F.: Using reputation measurement to defend mobile social networks against malicious feedback ratings. J. Supercomput. 71(6), 2190–2203 (2015)

    Article  Google Scholar 

  2. Wang, S., Huang, L., Hsu, C.H., Yang, F.: Collaboration reputation for trustworthy web service selection in social networks. J. Comput. Syst. Sci. 82(1), 130–143 (2016)

    Article  MathSciNet  Google Scholar 

  3. Thompson, P.: The digital natives as learners: technology use patterns and approaches to learning. Comput. Educ. 65, 12–33 (2013)

    Article  Google Scholar 

  4. Wang, R., Rho, S., Chen, B.W., Cai, W.: Modeling of large-scale social network services based on mechanisms of information diffusion: Sina weibo as a case study. Future Generation Computer Systems (2016)

  5. Lim, K.H., Datta, A.: Following the follower: detecting communities with common interests on twitter. In Proceedings of the 23rd ACM Conference on Hypertext and Social Media, pp. 317–318. ACM, Milwaukee (2012)

  6. Chen, B.-W., Wang, J.-C., Wang, J.-F.: A novel video summarization based on mining the story-structure and semantic relations among concept entities. IEEE Trans. Multimedia 11(2), 295–312 (2009)

    Article  Google Scholar 

  7. Bhattacharya, S., Henzinger, M., Nanongkai, D., Tsourakakis, C.: Space-and time-efficient algorithm for maintaining dense subgraphs on one-pass dynamic streams. In Proceedings of the Forty-Seventh Annual ACM on Symposium on Theory of Computing, pp. 173–182. ACM, Milwaukee (2015)

  8. Amelio, A., Pizzuti, C.: A cooperative evolutionary approach to learn communities in multilayer networks. In International Conference on Parallel Problem Solving from Nature, pp. 222–232. Springer, Cham (2014)

  9. Fagnan, J., Rabbany, R., Takaffoli, M., Verbeek, E., Zaïane, O.R.: Community dynamics: event and role analysis in social network analysis. In International Conference on Advanced Data Mining and Applications, pp. 85–97. Springer, Cham (2014)

  10. Chen, B.-W., Ji, W.: Intelligent marketing in smart cities: crowdsourced data for geo-conquesting. IEEE IT Prof. 18(4), 18–24 (2016)

    Article  Google Scholar 

  11. Larsson, A.O., Moe, H.: Studying political microblogging: Twitter users in the 2010 swedish election campaign. New Media Soc. 14(5), 729–747 (2012)

    Article  Google Scholar 

  12. Lim, K.H., Datta, A.: Finding twitter communities with common interests using following links of celebrities. In Proceedings of the 3rd International Workshop on Modeling Social Media, pp. 25–32. ACM, Milwaukee (2012)

  13. Kim, E., Sung, Y., Kang, H.: Brand followers retweeting behavior on twitter: how brand relationships influence brand electronic word-of-mouth. Comput. Hum. Behav. 37, 18–25 (2014)

    Article  Google Scholar 

  14. Bao, J., Zheng, Y., Wilkie, D., Mokbel, M.: Recommendations in location-based social networks: a survey. Geoinformatica 19(3), 525–565 (2015)

    Article  Google Scholar 

  15. Newmann, M.E.J.: Communities, modules and large-scale structure in networks. Nat. Phys. 8(1), 25–31 (2012)

    Article  Google Scholar 

  16. Newan, M.E.J.: Spectral methods for community detection and graph partitioning. Phys. Rev. E 88(4), 042822 (2013)

  17. Yang, J., Leskovec, J.: Defining and evaluating network communities based on ground-truth. Knowl. Inf. Syst. 42(1), 181–213 (2015)

    Article  Google Scholar 

  18. Le Martelot, E., Hankin, C.: Fast multi-scale detection of relevant communities in large-scale networks. Comput. J. bxt002 (2013)

  19. Li, J., Wang, X., Cui, Y.: Uncovering the overlapping community structure of complex networks by maximal cliques. Physica A: Stat. Mech. Appl. 415, 398–406 (2014)

    Article  MathSciNet  Google Scholar 

  20. Gopalan, P.K., Blei, D.M.: Efficient discovery of overlapping communities in massive networks. Proc. Natl. Acad. Sci. 110(36), 14534–14539 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  21. Zhang, Z., Li, Q., Zeng, D., Gao, H.: User community discovery from multi-relational networks. Decis. Support Syst. 54(2), 870–879 (2013)

  22. Chen, B.-W., Chen, C.-Y., Wang, J.-F.: Smart homecare surveillance system: behavior identification based on state transition support vector machines and sound directivity pattern analysis. IEEE T. Syst. Man Cy.: Syst. 43(6), 1279–1289 (2013)

  23. Chen, B.-W., Tsai, A.-C., Wang, J.-F.: Structuralized context-aware content and scalable resolution support for wireless VoD services. IEEE T. Consum. Electr. 55(2), 713–720 (2009)

  24. Takemura, S., Bharioke, A., Lu, Z., Nern, A., Vitaladevuni, S., Rivlin, P.K., Katz, W.T., Olbris, D.J., Plaza, S.M., Winston, P.: A visual motion detection circuit suggested by drosophila connectomics. Nature 500(7461), 175–181 (2013)

    Article  Google Scholar 

  25. Anandkumar, A., Liu, Y.k., Hsu, D.J., Foster, D.P., Kakade, S.M.: A spectral algorithm for latent dirichlet allocation. In Advances in neural information processing systems , pp. 917–925 (2012)

  26. Blei, D.M.: Probabilistic topic models. Commun. ACM. 55(4), 77–84 (2012)

    Article  Google Scholar 

  27. Wang, R., Cai, W., Shen, B.: The study of the dynamic model on KAD network information spreading. Telecommun. Syst. 1–9 (2015)

  28. Kappes, J., Andres, B., Hamprecht, F., Schnorr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B., Lellmann, J., Komodakis, N., et al.: A comparative study of modern inference techniques for discrete energy minimization problems. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1328–1335 (2013)

  29. Xie, J., Kelley, S., Szymanski, B.K.: Overlapping community detection in networks: the state-of-the-art and comparative study. ACM Comput. Surv. (csur) 45(4), 43 (2013)

    Article  MATH  Google Scholar 

  30. Wang, R., Cai, W.: A sequential game-theoretic study of the retweeting behavior in sina weibo. J. Supercomput. 71(9), 3301–3319 (2015)

    Article  Google Scholar 

  31. Ahn, Y.-Y., Bagrow, J.P., Lehmann, S.: Link communities reveal multiscale complexity in networks. Nature 466(7307), 761–764 (2010)

    Article  Google Scholar 

  32. Fan, J., Chen, X.: General clique percolation in random networks. EPL (Europhysics Letters) 107(2), 28005 (2014)

    Article  Google Scholar 

  33. Kim, Y., Jeong, H.: Map equation for link communities. Phys. Rev. E 84(2), 026110 (2011)

    Article  Google Scholar 

  34. Barabási, A.L., Albert, R.: Emergence of scaling in random networks. science 286(5439), 509–512 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  35. Lancichinetti, A., Fortunato, S., Kertész, J.: Detecting the overlapping and hierarchical community structure in complex networks. New J. Phys. 11(3), 033015 (2009)

    Article  Google Scholar 

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Authors and Affiliations

  1. Computer Science and Engineering, Northwestern Polytechnical University, Xi’an, 710072, People’s Republic of China

    Ru Wang & Wandong Cai

  2. Department of Media Software, Sungkyul University, Anyang-si, 430-742, Korea

    Seungmin Rho

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  1. Ru Wang
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  2. Seungmin Rho
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  3. Wandong Cai
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Correspondence to Ru Wang.

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Wang, R., Rho, S. & Cai, W. High-performance social networking: microblog community detection based on efficient interactive characteristic clustering. Cluster Comput 20, 1209–1221 (2017). https://doi.org/10.1007/s10586-017-0782-y

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  • DOI: https://doi.org/10.1007/s10586-017-0782-y

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