Abstract
The algorithms for discovering global community structure require the knowledge about entire network structures, which are still difficult and unrealistic to obtain from nowadays extremely large network. Several local algorithms that use local knowledge of networks to find the community for a given source node were proposed. However, these algorithms either require predefined thresholds which are hard to set manually or have lower precision rate. In this paper, we propose a novel method to discover local community for a given node. Firstly, we find the most similar node which is adjacent to the given node, and form the initial local community D together with the given node. Then, we calculate the connection degree of nodes belonging to D’s neighbors, and add the node whose connection degree is maximum to D if the local modularity measure will be increased. We evaluate our proposed method on well-known synthetic and real-world networks whose community structures are already given. The results of the experiment demonstrate that our algorithm is highly effective at discovering local community structure.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aaron, C., Newman, M., Cristopher, M.: Finding community structure in very large networks. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 70(6), 264–277 (2004)
Bagrow, J., Bolt, E.: A local method for detecting communities. Phys. Rev. E 72(4), 046108-1–046108-10 (2005)
Chen, Q., Wu, T.: A method for local community detection by finding maximal-degree nodes. In: ICMLC 2010, pp. 8–13 (2010)
Clauset, A.: Finding local community structure in networks. Phys. Rev. E 72(2), 026132 (2005)
Faloutsos, M., Faloutsos, P., Faloutsos, C.: On power-law relationships of the Internet topology. In: SIGCOMM 1999, pp. 251–262 (1999)
Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3/4/5), 75–174 (2009)
Girvan, M., Newman, M.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. U. S. A. 99(12), 7821–7826 (2002)
Jia, G., Cai, Z., Musolesi, M., Wang, Y., Tennant, D.A., Weber, R.J.M., Heath, J.K., He, S.: Community detection in social and biological networks using differential evolution. In: Dhaenens, C., Jourdan, L., Marmion, M.-E. (eds.) LION 2015. LNCS, vol. 8994, pp. 71–85. Springer, Heidelberg (2012). doi:10.1007/978-3-642-34413-8_6
Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark graphs for testing community detection algorithms. Phys. Rev. E 78(4), 046110-1–046110-5 (2008)
Liu, Y., Ji, X., Liu, C., et al.: Detecting local community structures in networks based on boundary identification. Math. Probl. Eng. 1–8 (2014). http://dx.doi.org/10.1155/2014/682015
Luo, F., Wang, J., Promislow, E.: Exploring local community structures in large networks. Web Intell. Agent Syst. (WIAS) 6(4), 387–400 (2008)
Lusseau, D.: The emergent properties of a dolphin social network. Proc. R Soc. Lond. Ser. B Biol. Sci. 270(Suppl. 2), S186–S188 (2003)
Ma, L., Huang, H., He, Q., Chiew, K., Wu, J., Che, Y.: GMAC: a seed-insensitive approach to local community detection. In: Bellatreche, L., Mohania, M.K. (eds.) DaWaK 2014. LNCS, vol. 8646, pp. 297–308. Springer, Heidelberg (2013). doi:10.1007/978-3-642-40131-2_26
Newman, M., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 69(2), 026113-1–026113-15 (2004)
Newman, M.: Fast algorithm for detecting community structure in networks. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 69(6), 066133-1–066133-5 (2004)
Newman, M.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103(23), 8577–8582 (2006). http://www-personal.umich.edu/~mejn/netdata/
Newman, M.: The structure of scientific collaboration networks. Proc. Natl. Acad. Sci. 98(2), 404–409 (2000)
Radicchi, F., Castellano, C., Cecconi, F., et al.: Defining and identifying communities in networks. Proc. Natl. Acad. Sci. U. S. A. 101(9), 2658–2663 (2004)
Shang, R., Bai, J., Jiao, L., et al.: Community detection based on modularity and an improved genetic algorithm. Phys. A 392(5), 1215–1231 (2013)
Takaffoli, M.: Community evolution in dynamic social networks - challenges and problems. In: ICDM Workshops 2011, pp. 1211–1214 (2011)
Tyler, J., Wilkinson, D., Huberman, B.: Email as spectroscopy: automated discovery of community structure within organizations. Inf. Soc. 21(2), 143–153 (2005)
Zachary, W.: An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33(4), 452–473 (1977)
Acknowledgments
The project is supported by National Natural Science Foundation of China (61370074, 61402091), the Fundamental Research Funds for the Central Universities of China under Grant N140404012.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Liu, J., Wang, D., Zhao, W., Feng, S., Zhang, Y. (2016). A Novel Approach for Discovering Local Community Structure in Networks. In: Sun, M., Huang, X., Lin, H., Liu, Z., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2016 2016. Lecture Notes in Computer Science(), vol 10035. Springer, Cham. https://doi.org/10.1007/978-3-319-47674-2_30
Download citation
DOI: https://doi.org/10.1007/978-3-319-47674-2_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47673-5
Online ISBN: 978-3-319-47674-2
eBook Packages: Computer ScienceComputer Science (R0)