Abstract
In the task of community detection, there often exists some useful prior information. In this paper, a Semi-supervised clustering approach using a new Evidential Label Propagation strategy (SELP) is proposed to incorporate the domain knowledge into the community detection model. The main advantage of SELP is that it can take limited supervised knowledge to guide the detection process. The prior information of community labels is expressed in the form of mass functions initially. Then a new evidential label propagation rule is adopted to propagate the labels from labeled data to unlabeled ones. The outliers can be identified to be in a special class. The experimental results demonstrate the effectiveness of SELP.
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
Denœux, T.: A \(k\)-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Trans. Syst. Man Cybern. 25(5), 804–813 (1995)
Denœux, T., Kanjanatarakul, O., Sriboonchitta, S.: EK-NNclus: a clustering procedure based on the evidential \(k\)-nearest neighbor rule. Knowl. Based Syst. 88, 57–69 (2015)
Li, M., Zhou, Z.-H.: SETRED: self-training with editing. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 611–621. Springer, Heidelberg (2005)
Liu, D., Bai, H.-Y., Li, H.-J., Wang, W.-J.: Semi-supervised community detection using label propagation. Int. J. Mod. Phys. B 28(29), 1450208 (2014)
Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76(3), 036106 (2007)
Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)
Wang, F., Zhang, C.: Label propagation through linear neighborhoods. IEEE Trans. Knowl. Data Eng. 20(1), 55–67 (2008)
Zhu, X., Lafferty, J., Rosenfeld, R.: Semi-supervised learning with graphs. Language Technologies Institute, School of Computer Science, Carnegie Mellon University (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhou, K., Martin, A., Pan, Q. (2016). Semi-supervised Evidential Label Propagation Algorithm for Graph Data. In: Vejnarová, J., Kratochvíl, V. (eds) Belief Functions: Theory and Applications. BELIEF 2016. Lecture Notes in Computer Science(), vol 9861. Springer, Cham. https://doi.org/10.1007/978-3-319-45559-4_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-45559-4_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-45558-7
Online ISBN: 978-3-319-45559-4
eBook Packages: Computer ScienceComputer Science (R0)