Username   Password       Forgot your password?  Forgot your username? 

A Community Structure Detection Method based on Field Effect

Volume 13, Number 6, October 2017 - Paper 16  - pp. 956-965
DOI: 10.23940/ijpe.17.06.p16.956965

Ru Zhang*, Zongwei Ren

School of Management, Harbin University of Commerce, Harbin, 150028, People’s Republic of China

(Submitted on May 18, 2017; Revised on August 20, 2017; Accepted on September 15, 2017)


The information expressed by vertex is influenced by the environment in the semantic social networks, which is the result of natural and social factors. The field effect theory can explain the relationship between social environment and psychological environment. Therefore,a community discovery based on field effect is proposed by the perspective of pattern classification. The algorithm based on secondary classification ideology can be simply described as follows: the social networks are first divided into several original communities based on networks structure and the results of classification are assigned to each vertex of the networks as the label; secondly, the labels spread based on field effect that is computed by natural and social factors; ultimately the vertices which have same labels can be divided into a community. It is a process of secondary classification that can reduce uncertainty of the labels setting and randomness of labels propagation effectively. Experimental results show that the improved algorithm can get better information similarity based on field effect of vertex and make the inner node more closely.


References: 18

    1. Y. Cha and J. Cho, “Social-Networks Analysis Using Topic Models,” in Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval (IACRDIR), pp. 565-574, New York, U.S, August 2012
    2. T. L. Griffiths and M. Steyvers , “Finding Scientific Topics,” Proceedings of the National Academy of Sciences, vol. 101, no. 1, pp. 5228-5235, April 2004
    3. M. Girvan and M. E. J. Newman, “Community Structure in Social and Biological Networks,” Proceedings of the National Academy, vol. 99, no. 12, pp. 7821-7826, Jun 2002
    4. L. Hao, S. H. Li, and Y. X. Zhao, “Detecting Community Structure Using Label Propagation with Weighted Coherent Neighborhood Propinquity,” Physica A: Statistical Mechanics and Its Applications, vol. 392, no. 14, pp. 3095-3105, July 2013
    5. T. Hastie, R. Tibshirani, and J. Friedman, “Hierarchical Clustering,” Psychometrika, vol. 43, no. 1, pp. 59–67, January 2009
    6. B. W. Kernighan and S.  Lin, “An Efficient Heuristic Procedure for Partitioning Graphs,” The Bell System Technical Journal, vol. 49, pp. 291-307, February 1970
    7. K. Lewin, “Principles of Topological Psychology,” Peking University Press, Beijing, China, October 2011
    8. A. Lancichinetti and S. Fortunato, “ Consensus Clustering in Complex Networks”,  Scientific Reports, vol. 2,  no. 13,  pp. 336-340, March 2012
    9. M. E. J. Newman and M. Girvan, “Finding and Evaluating Community Structure in Networks,” Physical Review, vol. 69, no. 2, pp. 1-15, February 2004
    10. H. Papadakis, C. Panagiotakis, and P. Fragopoulou, “Local Community Finding Using Synthetic Coordinates,” Future Information Technology, Berlin, German, vol. 185, pp. 9-15, 2011
    11. M. Rosvall and C. T. Bergstrom, “Maps of Random Walks on Complex Networks reveal Community Structure,” Proceedings of the National Academy of Sciences, vol. 105, no. 4, pp. 1118-1123, Jan 2008
    12. F. Radicchi, C. Castellano, F. Cecconi, V. Loreto, and D. Parisi, “Defining and Identifying Communities in Networks,” Proc. Natl. Acad. Sci. USA, vol. 101, no. 9, pp. 2658-2663, February 2004
    13. X. Xiong, X. Niu, G. Zhou, K. Xu, and Y. Z. Huang, “Microgroup Mining on TSina via Networks Structure and User Attribute,” in Proc. of the7th Int’l Conf. on Advanced Data Mining and Applications (ICADMA), pp. 138-151, Berlin, German, December 2011
    14. Y. Xin, J. Yang, and Z. Q. Xie, “An Overlapping Semantic Community Structure Detecting Algorithm by Label Propagation,” Zidonghua Xuebao/acta Automatica Sinica, vol. 40, no. 10, pp. 2262-2275, October 2014
    15. X. Xiong, G. Zhou, X. Niu, Y. Z. Huang, and K. Xu, “Remodeling the Networks for Microgroup Detection on Microblog,” Knowledge and Information Systems, vol. 39, no. 3, pp. 643-665, June 2014
    16. W. W. Zachary, “An Information Flow Model for Conflict and Fission in Small Groups,” Journal of Anthropological Research, vol. 33, no. 4, pp. 452-473, 1977
    17. X. P. Zhou, L. Xun, and H. Y. Zhang, “User Community Detection on Micro-Blog Using R-C model,” Journal of Software, no. 12, pp. 2808-2823, December 2014
    18. Z. X. Zhao, Y. Wang, J. T. Tian, and Z. X. Zhou, “A Novel Algorithm for Community Discovery in Social Networks Based on Label Propagation,” Journal of Computer Research and Development, vol. S3, pp. 8-15, January 2011


      Click here to download the paper.

      Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

      This site uses encryption for transmitting your passwords.