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Mathematical Problems in Engineering
Volume 2013, Article ID 385265, 13 pages
Research Article

Semisupervised Clustering for Networks Based on Fast Affinity Propagation

Department of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221008, China

Received 3 May 2013; Accepted 1 July 2013

Academic Editor: Orwa Jaber Housheya

Copyright © 2013 Mu Zhu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Most of the existing clustering algorithms for networks are unsupervised, which cannot help improve the clustering quality by utilizing a small number of prior knowledge. We propose a semisupervised clustering algorithm for networks based on fast affinity propagation (SCAN-FAP), which is essentially a kind of similarity metric learning method. Firstly, we define a new constraint similarity measure integrating the structural information and the pairwise constraints, which reflects the effective similarities between nodes in networks. Then, taking the constraint similarities as input, we propose a fast affinity propagation algorithm which keeps the advantages of the original affinity propagation algorithm while increasing the time efficiency by passing only the messages between certain nodes. Finally, by extensive experimental studies, we demonstrate that the proposed algorithm can take fully advantage of the prior knowledge and improve the clustering quality significantly. Furthermore, our algorithm has a superior performance to some of the state-of-art approaches.