Abstract
Community structure is one of the most significant properties of complex networks and is a foundational concept in exploring and analyzing networks. Researchers have concentrated partially on the topology information for community detection before, ignoring the prior information of the complex networks. However, background information can be obtained from the domain knowledge in many applications in advance. Especially, the labels of some nodes are already known, which indicates that a point exactly belongs to a specific category or does not belong to a certain one. Then, how to encode these individual labels into community detection becomes a challenging and interesting problem. In this paper, we present a semi-supervised framework based on non-negative matrix factorization, which can effectively incorporate the individual labels into the process of community detection. Promising experimental results on synthetic and real networks are provided to improve the accuracy of community detection.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Strogatz, S.H.: Exploring complex networks. J. Nature 410(6825), 268–276 (2001)
Newman, M.E.J.: Detecting community structure in networks. J. The European Physical Journal B-Condensed Matter and Complex Systems 38(2), 321–330 (2004)
Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. J Proceedings of the National Academy of Sciences 99(12), 7821–7826 (2002)
Newman, M.E.J.: Modularity and community structure in networks. J Proceedings of the National Academy of Sciences 103(23), 8577–8582 (2006)
Karrer, B., Newman, M.E.J.: Stochastic blockmodels and community structure in networks. J. Physical Review E 83(1), 016107 (2011)
Ma, X., Gao, L., Yong, X., et al.: Semi-supervised clustering algorithm for community structure detection in complex networks. J Physica A: Statistical Mechanics and its Applications 389(1), 187–197 (2010)
Eaton, E., Mansbach, R.: A Spin-Glass Model for Semi-Supervised Community Detection. AAAI (2012)
Zhang, Z.Y.: Community structure detection in complex networks with partial background information. J. EPL (Euro physics Letters) 101(4), 48005 (2013)
Zhang, Z.Y., Sun, K.D., Wang, S.Q.: Enhanced community structure detection in complex networks with partial background information. J. Scientific reports (2013)
Yang, L., Cao, X., Jin, D., et al.: A Unified Semi-Supervised Community Detection Framework Using Latent Space Graph Regularization. J (2014)
Nan, H., Wen-Yan, G.: Evaluate nodes importance in the network using data field theory. In: International Conference on Convergence Information Technology, pp. 1225–1234. IEEE (2007)
Freeman, L.C.: Centrality in social networks conceptual clarification. J. Social networks 1(3), 215–239 (1979)
Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. J. Nature 401(6755), 788–791 (1999)
Wang, R.S., Zhang, S., Wang, Y., et al.: Clustering complex networks and biological networks by non-negative matrix factorization with various similarity measures. J. Neurocomputing 72(1), 134–141 (2008)
Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. Advances in neural information processing systems, 556–562 (2001)
Zhong, S., Ghosh, J.: Generative model-based document clustering: a comparative study. J. Knowledge and Information Systems 8(3), 374–384 (2005)
Freeman, L.C.: A set of measures of centrality based on betweeness. J. Sociometry, 35–41 (1977)
Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark graphs for testing community detection algorithms. J. Physical review E 78(4), 046110 (2008)
Newman, M.E.J.: Modularity and community structure in networks. J Proceedings of the National Academy of Sciences 103(23), 8577–8582 (2006)
Adamic, L.A., Glance, N.: The political blogosphere and the 2004 US election: divided they blog. In: Proceedings of the 3rd international workshop on Link discovery, pp. 36–43. ACM (2005)
Newman, M.E.J.: Finding community structure in networks using the eigenvectors of matrices. J. Physical review E 74(3), 036104 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Wang, Z., Wang, W., Xue, G., Jiao, P., Li, X. (2015). Semi-supervised Community Detection Framework Based on Non-negative Factorization Using Individual Labels. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds) Advances in Swarm and Computational Intelligence. ICSI 2015. Lecture Notes in Computer Science(), vol 9141. Springer, Cham. https://doi.org/10.1007/978-3-319-20472-7_38
Download citation
DOI: https://doi.org/10.1007/978-3-319-20472-7_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20471-0
Online ISBN: 978-3-319-20472-7
eBook Packages: Computer ScienceComputer Science (R0)