Cluster Computing

, Volume 19, Issue 4, pp 1895–1905 | Cite as

The automatic estimating method of the in-degree of nodes in associated semantic network oriented to big data

  • Shunxiang Zhang
  • Xiaobo Yin
  • Congna He


Association Link Network (ALN) can organize massive news data to support many intelligent Web applications. The degree estimating can facilitate the rapid positioning of Web resources in ALN. In our prior work, we have well studied the degree estimating of out-degree of nodes in ALN. In this paper, we proposed an automatic estimating method of the in-degree of nodes in ALN to further reduce the searching scope for the rapid positioning. First, we explore the main factors of forming the in-degree of any one node from semantic feature view by qualitative analysis. Then, based on the result of qualitative analysis, we propose the model for estimating the in-degree of any one node in ALN, including the method framework, the first automatic estimating method and its further optimization method. Experimental results show that the proposed estimating method as well as the optimization method have a high precision.


Association Link Network In-degree Automatic estimating Optimization method Cluster computing 



This work was supported by the Natural Science Foundation of Anhui Province Universities (No. KJ2015A111, KJ2011Z098), in part by the National Science and Technology Major Project under Grant 2013ZX01033002-003, in part by the National Science Foundation of China under Grant 61300202, and in part by the Science Foundation of Shanghai under Grant 13ZR1452900.


  1. 1.
    Strogatz, S.H.: Exploring complex networks. Nature 410, 268–276 (2001)CrossRefGoogle Scholar
  2. 2.
    Albert, R., Jeong, H., Barabási, A.-L.: Diameter of the world-wide web. Nature 401, 130–131 (1999)CrossRefGoogle Scholar
  3. 3.
    Prokhorenkova, L.: General results on preferential attachment and clustering coefficient. Optim. Lett. (2016). doi: 10.1007/s11590-016-1030-8
  4. 4.
    Faloutsos, M., Faloutsos, P., Faloutsos, C.: On power-law relationships of the internet topology. Comp. Comm. Rev. 29, 251–262 (1999)CrossRefMATHGoogle Scholar
  5. 5.
    Kovács, I., Mizsei, R., Csermely, P.: A unified data representation theory for network visualization, ordering and coarse-graining. Sci. Rep. (Nature) 5(13786), 1–10 (2015)Google Scholar
  6. 6.
    Barzel, B., Liu, Y.-Y., Barabási, A.-L.: Constructing minimal models for complex system dynamics. Nat. Commun. 6(7186), 1–8 (2015)Google Scholar
  7. 7.
    Duncan, A., Liao, S., Vejchodsky, T., Erban, R., Grima, R.: Noise-induced multistability in chemical systems: discrete vs continuum modeling. Phys. Rev. 91(4), 042111 (2015)CrossRefGoogle Scholar
  8. 8.
    Kelleher, D.J., Reese, T.M., Yott, D.T., Brzoska, A.: Analysing properties of the C. Elegans neural network: mathematically modeling a biological system. Quant. Biol. (2011)Google Scholar
  9. 9.
    Luo, X.-F., Xu, Zh, Yu, J., et al.: Building association link network for semantic link on web resources. IEEE Trans. Autom. Sci. Eng. 8(3), 482–494 (2011)CrossRefGoogle Scholar
  10. 10.
    Zhang, S.X., Luo, X.F., Xuan, J.Y., Chen, X., Xu, W.M.: Discovering small-world in association link networks for association learning. World Wide Web 17(2), 229–254 (2014)CrossRefGoogle Scholar
  11. 11.
    Clauseta, A., Tanner, H.G., Abdallah, C.T., Byrne, R.H.: Controlling across complex networks-Emerging links between networks and control. Annu. Rev. Control 32, 183–192 (2008)CrossRefGoogle Scholar
  12. 12.
    Zhang, S.X., Wang, Y., Liu, W.D., Yin, X.B.: A model for estimating the out-degree of nodes in associated semantic network from semantic feature view. Concurr. Comput. 28(15), 4177–4193 (2016)CrossRefGoogle Scholar
  13. 13.
    Luo, X.-F., Fang, N., et al.: Semantic representation of scientific documents for the e-science Knowledge Grid. Concurr. Comput. Pract. Exp. 20(7), 839–862 (2008)CrossRefGoogle Scholar
  14. 14.
    Luo, X.-F., Fang, N.: Experimental study on the extraction and distribution of textual domain keywords. Concurr. Comput. Pract. Exp. 20, 1917–1932 (2008)CrossRefGoogle Scholar
  15. 15.
    Luo, X., Zhang, J., Ye, F., Wang, P., Cai, C.: Power Series Representation Model of Text Knowledge Based on Human Concept Learning. IEEE Transactions on Systems, Man, and Cybernetics: Systems 44(1), 86–102 (2014)Google Scholar
  16. 16.
    Liu, W.D., Luo, X.F., Gong, Z.G., Xuan, J.Y., Kou, N.M., Xu, Zh: Discovering the core semantics of event from social media. Future Gener. Comput. Syst. 64, 175–185 (2016)CrossRefGoogle Scholar
  17. 17.
    Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 International Conf. Management of Data (SIGMOD 93), pp. 207–216 (1993)Google Scholar
  18. 18.
    Jiang, T., Tan, A., Wang, K.: Mining generalized associations of semantic relations from textual web content. IEEE Trans. Knowl. Data Eng. 19(2), 164–179 (2007)CrossRefGoogle Scholar
  19. 19.
    Xu, Zh, et al.: Mining temporal explicit and implicit semantic relations between entities using web search engines. Future Gener. Comput. Syst. 37, 468–477 (2014)CrossRefGoogle Scholar
  20. 20.
    Hu, C., Xu, Zh, et al.: Semantic link network based model for organizing multimedia big data. IEEE Trans. Emerg. Topics Comput. 2(3), 376–387 (2014). doi: 10.1109/TETC.2014.2316525 MathSciNetCrossRefGoogle Scholar
  21. 21.
    Chen, Y.L., Li, F.Y., Fan, J.Q.: Mining association rules in big data with NGEP. Clust. Comput. 18(2), 577–585 (2015)CrossRefGoogle Scholar
  22. 22.
    Luo, X.-F., Hu, Q.-L.: Discovery of textual knowledge flow based on the management of knowledge maps. Concurr. Comput. Pract. Exp. 20, 1791–1806 (2008)CrossRefGoogle Scholar
  23. 23.
    Zhang, S.X., Lu, K., Liu, W., Yin, X., Zhu, G.: Generating associated knowledge flow in large-scale web pages based on user interaction. Comput. Syst. Sci. Eng. 30(5), 377–389 (2015)Google Scholar
  24. 24.
    Yen, N.Y., Park, J.J.J.H., Jin, Q., Shih, T.K.: Modeling user-generated contents: an intelligent state machine for user-centric search support. Pers. Ubiquitous Comput. 17(8), 1731–1739 (2013)CrossRefGoogle Scholar
  25. 25.
    Li, Q., Lau, R., Shih, T.-K. et al.: Technology supports for distributed and collaborative learning over the internet. ACM Trans. Internet Technol. 8(2): 10:1–10:24 (2008)Google Scholar
  26. 26.
    Xu, Zh, et al.: Semantic based representing and organizing surveillance big data using video structural description technology. J. Syst. Softw. 102, 217–225 (2015)CrossRefGoogle Scholar
  27. 27.
    Xu, Zh, et al.: Generating temporal semantic context of concepts using web search engines. J. Netw. Comput. Appl. 43, 42–55 (2014)CrossRefGoogle Scholar
  28. 28.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of “Small-World” networks. Nature 393, 440–442 (1998)CrossRefGoogle Scholar
  29. 29.
    Kleinberg, J.M.: Navigation in a small world. Nature 406, 845 (2000)CrossRefGoogle Scholar
  30. 30.
    Fronczak, A., Fronczak, P., Hołyst, J.A.: Average path length in random networks. Phys. Rev. E 70, 056110-1–056110-7 (2004)CrossRefMATHGoogle Scholar
  31. 31.
    Newman, M.E.J.: The structure and function of complex networks. SIAM Rev. 45, 167–256 (2003)MathSciNetCrossRefMATHGoogle Scholar
  32. 32.
    Schich, M., Song, C., Ahn, Y.Y., Mirsky, A., Martino, M., Barabási, A.-L., Helbing, D.: A network framework of cultural history. Science 345, 558–562 (2014)CrossRefGoogle Scholar
  33. 33.
    Xuan, J.Y., Jie, L., Zhang, G.Q., Luo, X.F.: Topic model for graph mining. IEEE Trans. Cybern. 45(12), 2792–2803 (2015)CrossRefGoogle Scholar
  34. 34.
    Jin, S., Lin, W., Yin, H., Yang, S., Li, A., Deng, B.: Community structure mining in big data social media networks with MapReduce. Clust. Comput. 18(3), 999–1010 (2015)CrossRefGoogle Scholar
  35. 35.
    Jiang, H., Chen, Y., Qiao, Zh, Weng, T.-H., Li, K.-C.: Scaling up MapReduce-based big data processing on multi-GPU systems. Clust. Comput. 18(1), 369–383 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Anhui University of Science & TechnologyHuainanChina
  2. 2.Changzhou Institute of Light Industry TechnologyChangzhouChina
  3. 3.School of Computer Engineering and ScienceShanghai UniversityShanghaiChina

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