Abstract
Local community detection aims at discovering a community from a seed node by maximizing a given goodness metric. This problem has attracted a lot of attention, and various goodness metrics have been proposed in recent years. However, most existing approaches are based on the assumption that either nodes or edges in network have equal weight. In fact, the usage of weights of both nodes and edges in network can somewhat enhance the algorithmic accuracy. In this paper, we propose a novel approach for local community detection via edge weighting. In detail, we first design a new node similarity measure with full consideration of adjacent nodes’ weights. We next develop an edge weighting method based on this similarity measure. Then, we define a new goodness metric to quantify the quality of local community by integrating the edge weights. In our algorithm, we discover local community by giving priority to shell node which has maximal similarity with the current local community. We evaluate the proposed algorithm on both synthetic and real-world networks. The results of our experiment demonstrate that our algorithm is highly effective at local community detection compared to related algorithms.
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. Nonlin. Soft Matter Phys. 70(6), 264–277 (2004)
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(3/5), 75–174 (2010)
Girvan, M., Newman, M.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. USA 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.A., Weber, R.J., Heath, J.K., He, S.: Community detection in social and biological networks using differential evolution. In: Hamadi, Y., Schoenauer, M. (eds.) LION 2012. LNCS, vol. 7219, pp. 71–85. Springer, Heidelberg (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, Y., Ji, X., Liu, C., et al.: Detecting local community structures in networks based on boundary identification. In: Mathematical Problems in Engineering, pp. 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: DaWaK, pp. 297–308 (2013)
Newman, M.: The structure of scientific collaboration networks. Work. Pap. 98(2), 404–409 (2000)
Newman, M.: Fast algorithm for detecting community structure in networks. Phys. Rev. E Stat. Nonlin. 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. Nonlin. 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. USA 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 21st 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 2011, 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 2015, 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 (61172168).
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
Zhao, W., Zhang, F., Liu, J. (2016). Local Community Detection via Edge Weighting. In: Ma, S., et al. Information Retrieval Technology. AIRS 2016. Lecture Notes in Computer Science(), vol 9994. Springer, Cham. https://doi.org/10.1007/978-3-319-48051-0_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-48051-0_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48050-3
Online ISBN: 978-3-319-48051-0
eBook Packages: Computer ScienceComputer Science (R0)