Social Networks Mining Based on Information Retrieval Technologies and Bees Swarm Optimization: Application to DBLP

  • Drias Yassine
  • Drias Habiba
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 206)


Unlike the previous works where detecting communities is performed on large graphs, our approach considers textual documents for discovering potential social networks. More precisely, the aim of this paper is to extract social communities from a collection of documents and a query specifying the domain of interest that may link the group. We propose a methodology which develops an information retrieval system capable to generate the documents that are in relationship with any topic. The authors of these documents are linked together to constitute the social community around the given thematic. The search process in the information retrieval system is designed using a bee swarm optimization method in order to optimize the retrieval time. Our approach was implemented and tested on CACM and DBLP and the time of building a social network is quasi instant.


social network knowledge mining information retrieval BSO DBLP 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Christopher, D.M., Prabhakar, R., Hinrich, S.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)zbMATHGoogle Scholar
  2. 2.
    Clauset, A., Newman, M., Moore, C.: Finding community structure in very large networks. Physical Review E 70, 066111 (2004)CrossRefGoogle Scholar
  3. 3.
    Drias, H., Sadeg, S., Yahi, S.: Cooperative Bees Swarm for Solving the Maximum Weighted Satisfiability Problem. In: Cabestany, J., Prieto, A.G., Sandoval, F. (eds.) IWANN 2005. LNCS, vol. 3512, pp. 318–325. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Drias, H., Mosteghanemi, H.: Bees Swarm Optimization based Approach for Web Information Retrieval. In: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, pp. 6–13 (2010)Google Scholar
  5. 5.
    Flake, G., Lawrence, S., Giles, C.: Efficient identification of web communities. In: KDD 2000, pp. 150–160 (2000)Google Scholar
  6. 6.
    Fortunato, S.: Community detection in graphs. Physics Reports 486(3-5), 75–174 (2010)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Leskovec, J., Lang, K., Dasgupta, A., Mahoney, M.: Statistical properties of community structure in large social and information networks. In: WWW 2008: Proceedings of the 17th International Conference on World Wide Web, pp. 695–704 (2008)Google Scholar
  8. 8.
    Moldovan, D.I., Mihalcea, R.: Using WordNet and lexical operators to improve internet searches. IEEE Internet Computing 4(1), 34–43 (2000)CrossRefGoogle Scholar
  9. 9.
    Newman, M., Girvan, M.: Finding and evaluating community structure in networks. Physical Review E 69, 026113 (2004)CrossRefGoogle Scholar
  10. 10.
    Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, pp. 57–66. ACM Press (2001)Google Scholar
  11. 11.
    Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., Parisi, D.: Defining and identifying communities in networks. Proceedings of the National Academy of Sciences of the United States of America 101(9), 2658–2663 (2004)CrossRefGoogle Scholar
  12. 12.
    Van Rijsbergen, C.J.: Information Retrieval, 2nd edn. Butterworth, London (1979)Google Scholar
  13. 13.
    Salton, G.: The SMART retrieval system: experiments in automatic document processing. Prentice-Hall, Englewood Cliffs (1976)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Computer ScienceUSTHB, LRIAAlgiersAlgeria

Personalised recommendations