Abstract
Complex network is an active research field in complex system in recent years. In this paper, we investigate the topological structure of complex networks and present a novel unsupervised visual clustering algorithm for finding community in complex networks. We firstly introduce a new distance between nodes to measure the dissimilarity between nodes and obtain the distance matrix. Then the rows (columns) of distance matrix are reordered according to the dissimilarity and the reordered matrix is displayed as an intensity image. Clusters are indicated by dark blocks of pixels along the main diagonal. The experiments show that our algorithm has good performance and can find the community structure hidden in complex networks.
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.
Similar content being viewed by others
References
Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R.: Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press (1996)
Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression. Kluwer Academic, Boston (1992)
Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. John Wiley & Sons, New York (1973)
Watts, D., Strogatz, S.: Collective dynamics of ’small-world’ networks. Nature 393, 440–442 (1998)
Barabasi, A.-L., Albert, R.: Emergence of scaling in random networks. Science 286, 509–512 (1999)
Newman, M.E.J.: Detecting Community Structure in Networks. Eur. Phys. J. B 38, 321–330 (2004)
Bezdek, J.C., Hathaway, R.J.: VAT: A Tool for Visual Assessment of (Cluster) Tendency. In: Proc. IJCNN 2002, pp. 2225–2230. IEEE Press, Piscataway, NJ (2002)
Auber, D., Chiricota, Y., Jourdan, F., Melancon, G.: Multiscale visualization of small world networks. In: Proceedings of the 2003 IEEE Symposium on Information Visualization, pp. 75–81 (2003)
Holme, P., Kim, B.J.: Growing scale-free networks with tunable clustering. Phys. Rev. EÂ 65, 026107 (2001)
Klemm, K., EguÃluz, V.M.: Growing Scale-Free Networks with Small World Behavior. Phys. Rev. E. 65, 057102 (2002)
Li, C., Maini, P.K.: An Evolving Network Model with Community Structure. Journal of Physics A: Mathematical and General 38(45), 9741–9749 (2005)
Kanungo, et al.: An Efficient k-Means Clustering Algorithm: Analysis and Implementation. PAMI (24) (7), 881–892 (2002)
Yu, J.: [Jian]: General C-Means Clustering Model. PAMI (27) (8), 1197–1211 (2005)
Bradley, P.S., Fayyad, U.M.: Refining Initial Points for K-Means Clustering. In: Proceedings of the Fifteenth International Conference on Machine Learning, pp. 91–99 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yang, S., Luo, S., Li, J. (2006). A Novel Visual Clustering Algorithm for Finding Community in Complex Network. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_44
Download citation
DOI: https://doi.org/10.1007/11811305_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37025-3
Online ISBN: 978-3-540-37026-0
eBook Packages: Computer ScienceComputer Science (R0)