Abstract
Local community detection aims at finding a local community from a start node in a network without requiring the global network structure. Similarity-based local community detection algorithms have achieved promising performance, but still suffer from high computational complexity. In this paper, we first design a unified local community detection framework for fusing different node similarity measurement. Based on this framework, we implement eight local community detection algorithms by utilizing different node similarity measurements. We test these algorithms on both synthetic and real-world network datasets. The experimental results show that the local community detection algorithms implemented in our framework are better at detecting local community compared with related algorithms. That means the performance of discovering local community would be largely improved by using good node similarity measurements. This work provides a novel view to evaluate similarity measurements, which can be further applied to link prediction, recommendation system and so on.
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
Bagrow, J., Bolt, E.: A local method for detecting communities. Phys. Rev. E 72(4), 046108-1–046108-10 (2005)
Clauset, A.: Finding local community structure in networks. Phys. Rev. E 72(2), 026132 (2005)
Clauset, A., Newman, M.E., Moore, C.: Finding community structure in very large networks. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 70(6), 264–277 (2004)
Faloutsos, M., Faloutsos, P., Faloutsos, C.: On Power-law relationships of the internet topology. In: SIGCOMM, pp. 251–262 (1999)
Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3/5), 75–174 (2010)
Girvan, M., Newman, M.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. U.S. Am. 99(12), 7821–7826 (2002)
Huang, J., Sun, H., Liu, Y., Song, Q., Weninger, T.: Towards online multiresolution community detection in large-scale networks. PLoS ONE 6(8), 492 (2011)
Jia, G., Cai, Z., Musolesi, M., Wang, Y., Tennant, D., Weber, R., Heath, J., He, S.: Community detection in social and biological networks using differential evolution. In: LION, pp. 71–85 (2012)
Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark graphs for testing community detection algorithms. Phys. Rev. E 78(4), 046110-1–046110-5 (2008)
Liu, J., Hou, L., Pan, X., Guo, Q., Zhou, T.: Stability of similarity measurements for bipartite networks. Scientific reports (6) (2016)
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)
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, Mukesh K. (eds.) DaWaK 2013. LNCS, vol. 8057, pp. 297–308. Springer, Heidelberg (2013). doi:10.1007/978-3-642-40131-2_26
Newman, M.: The structure of scientific collaboration networks. Working Pap. 98(2), 404–409 (2000)
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., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 69(2), 026113-1–026113-15 (2004)
Radicchi, F., Castellano, C., Cecconi, F., et al.: Defining and identifying communities in networks. Proc. Natl. Acad. Sci. U.S. Am. 101(9), 2658–2663 (2004)
Schaeffer, S.: Graph clustering. Comput. Sci. Rev. (CSR) 1(1), 27–64 (2007)
Shao, J., Han, Z., Yang, Q., Zhou, T.: Community detection based on distance dynamics. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1075–1084 (2015)
Takaffoli, M.: Community evolution in dynamic social networks - challenges and problems. In: ICDM Workshops, pp. 1211–1214 (2011)
Tyler, J.R., Wilkinson, D.M., Huberman, B.A.: Email as spectroscopy: automated discovery of community structure within organizations. Inf. Soc. 21(2), 143–153 (2005)
Wu, Y., Huang, H., Hao, Z., Chen, F.: Local community detection using link similarity. J. Comput. Sci. Technol. (JCST) 27(6), 1261–1268 (2012)
Wu, Y., Jin, R., Li, J., Zhang, X.: Robust local community detection: on free rider effect and its elimination. In: VLDB, pp. 798–809 (2015)
Zachary, W.: An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33(4), 452–473 (1977)
Zhou, T., Lü, L., Zhang, Y.: Predicting missing links via local information. Eur. Phys. J. B 71(4), 623–630 (2009)
Acknowledgments
The project is supported by National Natural Science Foundation of China (61370074, 61402091).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Liu, J., Wang, D., Zhao, W., Feng, S., Zhang, Y. (2017). A Unified Framework of Lightweight Local Community Detection for Different Node Similarity Measurement. In: Cheng, X., Ma, W., Liu, H., Shen, H., Feng, S., Xie, X. (eds) Social Media Processing. SMP 2017. Communications in Computer and Information Science, vol 774. Springer, Singapore. https://doi.org/10.1007/978-981-10-6805-8_23
Download citation
DOI: https://doi.org/10.1007/978-981-10-6805-8_23
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-6804-1
Online ISBN: 978-981-10-6805-8
eBook Packages: Computer ScienceComputer Science (R0)