Skip to main content
SpringerLink
Log in

Local Community Detection Using Link Similarity

  • Short Paper
  • Published:
Journal of Computer Science and Technology Aims and scope Submit manuscript

Abstract

Exploring local community structure is an appealing problem that has drawn much recent attention in the area of social network analysis. As the complete information of network is often difficult to obtain, such as networks of web pages, research papers and Facebook users, people can only detect community structure from a certain source vertex with limited knowledge of the entire graph. The existing approaches do well in measuring the community quality, but they are largely dependent on source vertex and putting too strict policy in agglomerating new vertices. Moreover, they have predefined parameters which are difficult to obtain. This paper proposes a method to find local community structure by analyzing link similarity between the community and the vertex. Inspired by the fact that elements in the same community are more likely to share common links, we explore community structure heuristically by giving priority to vertices which have a high link similarity with the community. A three-phase process is also used for the sake of improving quality of community structure. Experimental results prove that our method performs effectively not only in computer-generated graphs but also in real-world graphs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Parthasarathy S, Ruan Y, Satuluri V. Community discovery in social networks: Applications, methods and emerging trends. In Social Network Data Analytics, Aggarwal C (ed.), 2011, pp.79–113.

  2. Tang W, Zhuang H, Tang J. Learning to infer social ties in large networks. In Proc. the 2011 European Conf. Machine Learning and Knowledge Discovery in Databases, Sept. 2011, Part 3, pp.381–397.

  3. Kumar R, Raghavan P, Rajagopalan S, Sivakumar D, Tompkins A, Upfal E. The web as a graph. In Proc. the 19th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, May 2000, pp.1–10.

  4. Tang J, Zhang J, Yao L, Li J, Zhang L, Su Z. Arnetminer: Extraction and mining of academic social networks. In Proc. the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 2008, pp.990–998.

  5. Clauset A, Newman M, Moore C (2004) Finding community structure in very large networks. Physical review E 70(6):066111

    Article  Google Scholar 

  6. Rattigan M, Maier M, Jensen D. Graph clustering with network structure indices. In Proc. the 24th International Conference on Machine Learning, June 2007, pp.783–790.

  7. Von Luxburg U (2007) A tutorial on spectral clustering. Statistics and Computing 17(4):395–416

    Article  MathSciNet  Google Scholar 

  8. Pan J J, Yang Q. Co-localization from labeled and unlabeled data using graph laplacian. In Proc. the 20th Int. Joint Conf. Arti¯cial Intelligence, Jan. 2007, pp.2166–2171.

  9. Fortunato S (2010) Community detection in graphs. Physics Reports 486(3/5):75–174

    Article  MathSciNet  Google Scholar 

  10. Su Z, Yang Q, Zhang H, Xu X, Hu Y. Correlation-based document clustering using web logs. In Proc. the 34th Annual Hawaii Int. Conf. System Sciences, 2001, Vol.5, p.5022.

  11. Kriegel H, Kröger P, Zimek A. Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering. ACM Transactions on Knowledge Discovery from Data, 2009, 3(1), Article No.1.

    Google Scholar 

  12. Liu N, Yang Q. Eigenrank: A ranking-oriented approach to collaborative filtering. In Proc. the 31st Int. Conf. Research and Develop. Inform. Retrieval, July 2008, pp.83–90.

  13. Hotho A, Jäschke R, Schmitz C, Stumme G. Information retrieval in folksonomies: Search and ranking. In Proc. the 3rd European Conf. The Semantic Web: Research and Applications, June 2006, pp.411–426.

  14. Newman M (2004) Detecting community structure in networks. The European Physical Journal B-Condensed Matter and Complex Systems 38(2):321–330

    Article  Google Scholar 

  15. Bagrow J, Bollt E (2005) Local method for detecting communities. Physical Review E 72(4):046108

    Article  Google Scholar 

  16. Clauset A (2005) Finding local community structure in networks. Physical Review E 72(2):026132

    Article  Google Scholar 

  17. Luo F, Wang J, Promislow E (2008) Exploring local community structures in large networks. Web Intelligence and Agent Systems 6(4):387–400

    Google Scholar 

  18. Bagrow J (2008) Evaluating local community methods in networks. Journal of Statistical Mechanics: Theory and Experiment 2008:P05001

    Article  Google Scholar 

  19. Chen J, Zaïane O, Goebel R. Local community identification in social networks. In Proc. the 2009 Int. Conf. Advances in Social Network Analysis and Mining, July 2009, pp.237–242.

  20. Zhang X, Wang L, Li Y, Liang W. Extracting local community structure from local cores. In Proc. the 16th Int. Conf. Database Systems for Advanced Applications, April 2011, pp.287–298.

  21. Rosvall M, Bergstrom C (2008) Maps of random walks on complex networks reveal community structure. Proc the National Academy of Sciences of the United States of America 105(4):1118–1123

    Article  Google Scholar 

  22. Radicchi F, Castellano C, Cecconi F, Loreto V, Parisi D (2004) Defining and identifying communities in networks. Proc the National Academy of Sciences of the United States of America 101(9):2658–2663

    Article  Google Scholar 

  23. Andersen R. A local algorithm for finding dense subgraphs. ACM Transactions on Algorithms, 2010, 6(4), Article No. 60.

  24. Andersen R, Lang K. An algorithm for improving graph partitions. In Proc. the 19th Annual ACM-SIAM Symposium on Discrete Algorithms, Jan. 2008, pp.651–660.

  25. Andersen R, Lang K. Communities from seed sets. In Proc. the 15th International Conference on World Wide Web, May 2006, pp.223–232.

  26. Riedy J, Bader D, Jiang K, Pande P, Sharma R. Detecting communities from given seeds in social networks. Technical Report GT-CSE-11-01, Georgia Institute of Technology, Feb. 2011.

  27. Flake G, Lawrence S, Giles C. Efficient identification of Web communities. In Proc. the 6th Int. Conf. Knowledge Discovery and Data Mining, Aug. 2000, pp.150–160.

  28. Girvan M, Newman M (2002) Community structure in social and biological networks. Proc the National Academy of Sciences of the United States of America 99(12):7821–7826

    Article  MathSciNet  MATH  Google Scholar 

  29. Xu X, Yuruk N, Feng Z, Schweiger T. Scan: A structural clustering algorithm for networks. In Proc. the 13th Int. Conf. Knowledge Discovery and Data Mining, Aug. 2007, pp.824–833.

  30. McCallum A, Nigam K, Rennie J, Seymore K (2000) Automating the construction of internet portals with machine learning. Information Retrieval 3(2):127–163

    Article  Google Scholar 

  31. Lu Y, Zhang L, Liu J, Tian Q (2010) Constructing concept lexica with small semantic gaps. IEEE Transactions on Multimedia 12(4):288–299

    Article  Google Scholar 

  32. Yang J, Cai R, Wang Y, Zhu J, Zhang L, Ma W. Incorporating site-level knowledge to extract structured data from web forums. In Proc. the 18th International Conference on World Wide Web, April 2009, pp.181–190.

  33. Li X, Wang Y, Acero A. Learning query intent from regularized click graphs. In Proc. the 31st Int. Conf. Research and Development in Information Retrieval, July 2008, pp.339–346.

Download references

Author information

Authors and Affiliations

  1. School of Software Engineering, South China University of Technology, Guangzhou, 510000, China

    Ying-Jun Wu & Han Huang

  2. Department of Management Sciences, College of Business, City University of Hong Kong, Hong Kong, China

    Han Huang

  3. Faculty of Computer Science, Guangdong University of Technology, Guangzhou, 510000, China

    Zhi-Feng Hao

  4. School of Journalism and Communication, South China University of Technology, Guangzhou, 510000, China

    Feng Chen

Authors
  1. Ying-Jun Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Han Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhi-Feng Hao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Feng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Han Huang.

Additional information

This work was supported by the National Natural Science Foundation of China under Grant No. 61170193, the Doctoral Program of the Ministry of Education of China under Grant No. 20090172120035, the Natural Science Foundation of Guangdong Province of China under Grant No. S2012010010613, the Fundamental Research Funds for the Central Universities of South China University of Technology of China under Grant No. 2012ZM0087, the Pearl River Science & Technology Start Project of China under Grant No. 2012J2200007.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wu, YJ., Huang, H., Hao, ZF. et al. Local Community Detection Using Link Similarity. J. Comput. Sci. Technol. 27, 1261–1268 (2012). https://doi.org/10.1007/s11390-012-1302-4

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11390-012-1302-4

Keywords

Search

Navigation