Abstract
Label propagation algorithm has been proved to be an effective method for community detection in large-scale complex networks. Though many effects have been devoted to improve the original label propagation algorithm, its robustness has still not been well addressed. The random update strategy of node’s label not only affects the robustness of the algorithm, but also the stability and consistency of community discovery. In this paper, we propose a robust label propagation algorithm to overcome this defect and apply it to community detection by modify update policy. As the experimental results on the real social network indicated, besides maintaining the simplicity of the original algorithm, the proposed algorithm also improves its stability and performance. The results on Sina-Microblog data set have verified that structural features of online social network have close relationship with its semantic features.
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
Preview
Unable to display preview. Download preview PDF.
References
Han, J.W., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann Publishers, San Francisco (2005)
Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70(6) (2004)
Blondel, V.D., Guillaume, J.-L., Lambiotte, R.: Fast unfolding of community hierarchies in large networks. Journal of Statistical Mechanics: Theory and Experiment 10(10008) (2008)
Boccaletti, S., Ivanchenko, M., Latora, V.: Detecting complex network modularity by dynamic clustering. Phys. Rev. E 75(4) (2007)
Donetti, L., Muñoz, M.A.: Detecting network communities: a new systematic and efficient algorithm (2009), http://arxiv.org/abs/conmat/0404652
Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Physical Review E 76(036106) (2007)
Tibely, G., Kertesz, J.: Note on the equivalence of the label propagation method of community detection and a potts model approach. Physical Review E (0803.2804) (2008)
Barber, M., Clark, J.W.: Detecting network communities by propagation labels under constrains. Physical Review E 80(026129) (2009)
Leung, I.X.Y., Hui, P., Li, P., Crowcroft, J.: Towards real-time community detection in large network. Phys. Rev. E (79066107) (2009)
Šubel, L., Bajec, M.: Unfolding communities in large complex networks combining defensive and offensive label propagation. In: Proceeding of the ECML PKDD Workshop on the Analysis of Complex Networks, pp. 87–104 (2011)
Šubel, L., Bajec, M.: Robust network community detection using balanced propagation.CoRR abs/1106.5524 (2011)
Newman, M.E.J., Girvan, M.: Finding and evaluation community structure in networks. Phys. Rev. E 69 (2004)
Manning, C.D., Raghavan, P., Schutze, H.: Introduction to information retrieval. Cambridge University Press (2008)
Fan, P., Li, P., Jiang, Z., Li, W., Wang, H.: Measurement and analysis of topology and information propagation on sina-mircoblog. In: The 3rd International Workshop on Social Computing (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Xu, B., Liang, Z., Jia, Y., Zhou, B. (2013). Community Detection Based on Robust Label Propagation Algorithm. In: Yuan, Y., Wu, X., Lu, Y. (eds) Trustworthy Computing and Services. ISCTCS 2012. Communications in Computer and Information Science, vol 320. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35795-4_38
Download citation
DOI: https://doi.org/10.1007/978-3-642-35795-4_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35794-7
Online ISBN: 978-3-642-35795-4
eBook Packages: Computer ScienceComputer Science (R0)