Abstract
Detecting communities in complex networks is of great importance in sociology, biology and computer science, disciplines where systems are often represented as networks. In this paper, we use the coarse-grained-diffusion-distance based agglomerative algorithm to uncover the community structure exhibited by sample networks generated from Gaussian mixture model, in which the connectivity of the network is induced by a metric. The present algorithm can identify the community structure in a high degree of efficiency and accuracy. An appropriate number of communities can be automatically determined without any prior knowledge about the community structure. The computational results on three artificial networks confirm the capability of the algorithm.
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
Albert, R., Barabási, A.L.: Statistical mechanics of complex networks. Rev. Mod. Phys. 74(1), 47–97 (2002)
Newman, M.: The structure and function of complex networks. SIAM Review 45(2), 167–256 (2003)
Newman, M., Barabási, A.L., Watts, D.J.: The structure and dynamics of networks. Princeton University Press, Princeton (2005)
Barabási, A., Jeong, H., Neda, Z., Ravasz, E., Schubert, A., Vicsek, T.: Evolution of the social network of scientific collaborations. Physica A 311, 590–614 (2002)
Ravasz, E., Somera, A., Mongru, D., Oltvai, Z., Barabási, A.: Hierarchical organization of modularity in metabolic networks. Science 297(5586), 1551–1555 (2002)
Girvan, M., Newman, M.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. USA 99(12), 7821–7826 (2002)
Newman, M., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. EÂ 69(2), 026113 (2004)
Newman, M.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. USA 103(23), 8577–8582 (2006)
E, W., Li, T., Vanden-Eijnden, E.: Optimal partition and effective dynamics of complex networks. Proc. Natl. Acad. Sci. USA 105(23), 7907–7912 (2008)
Li, T., Liu, J., E, W.: Probabilistic Framework for Network Partition. Phys. Rev. EÂ 80, 26106 (2009)
Liu, J., Liu, T.: Detecting community structure in complex networks using simulated annealing with k-means algorithms. Physica A 389(11), 2300–2309 (2010)
Liu, J., Liu, T.: Coarse-grained diffusion distance for community structure detection in complex networks. J. Stat. Mech. 12, P12030 (2010)
Fortunato, S.: Community detection in graphs. Phys. Rep. 486, 75–174 (2010)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intel. 22(8), 888–905 (2000)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York (2001)
Penrose, M.: Random Geometric Graphs. Oxford University Press, Oxford (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhao, L., Liu, T., Liu, J. (2011). Community Detection in Sample Networks Generated from Gaussian Mixture Model. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds) Advances in Swarm Intelligence. ICSI 2011. Lecture Notes in Computer Science, vol 6729. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21524-7_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-21524-7_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21523-0
Online ISBN: 978-3-642-21524-7
eBook Packages: Computer ScienceComputer Science (R0)