Abstract
In this paper we address the problem of detecting communities or clusters in networks. An efficient hierarchical clustering algorithm based on a global similarity metric is introduced. The technique exploits several characteristic average values of the similarity function. Also an analytical result is provided for the average similarity of vertices on an Erdos-Renyi graph in the asymptotic limit of the graph size. Finaly the performance of the algorithm is evaluated over a set of computer-generated graphs. Our analysis shows that newly proposed algorithm is superior when compared to the popular algorithm of Girvan and Newman and has equal or lower running time.
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
Bornholdt, S., Schuster, H.G. (eds.): Handbook of graphs and networks: from the Genome to the Internet. Wiley VCH, Weinheim (2002)
Newman, M.E.J.: The structure and function of complex networks. SIAM Review 45, 167–256 (2003)
Barrat, A., Barthelemy, M., Vespignani, A.: Dynamical processes on complex networks. Cambridge University Press, Cambridge (2008)
Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proceedings of the National Academy of Sciences 99, 7821–7826 (2002)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 (2004)
Boss, M., Elsinger, H., Summer, M., Thurner, S.: The network topology of the Interbank market, http://arxiv.org/abs/cond-mat/0309582
Guimera, R., Amaral, L.A.N.: Functional cartography of complex metabolic networks. Nature 433, 895–900 (2005)
Holme, P., Huss, M., Jeong, H.: Subnetwork hierarchies of biochemical pathways. Bioinformatics 19, 532–538 (2003)
Flake, G.W., Lawrence, S.R., Giles, C.L., Coetzee, F.M.: Self-organization and identification of Web communities. IEEE Computer 35, 66–71 (2002)
Ravasz, E., Somera, A.L., Mongru, D.A., Oltavi, Z.N., Barabasi, A.-L.: Hierarchical organization of modularity in metabolic networks. Science 297, 1551–1555 (2002)
Palla, G., Derenyi, I., Farkas, I., Viscek, T.: Uncovering the overlapping community structure in complex networks in nature and society. Nature 435, 814–818 (2005)
Duch, J., Arenas, A.: Community detection in complex networks using extremal optimization. Phys. Rev. E 72, 027104 (2005)
Newman, M.E.J.: Modularity and community structure in networks. Proceedings of the National Academy of Sciences 103, 8577–8582 (2006)
Donetti, L., Munoz, M.: Detecting network communities: a new systematic and efficient algorithm. J. Stat. Mech., P10012 (2004)
Reichardt, J., Bornholdt, S.: Detecting fuzzy community structures in complex networks with a Potts model. Phys. Rev. Lett. 93, 218701 (2004)
Zhou, H.: Distance, dissimilarity index, and network community structure. Phys. Rev. E 67, 061901 (2003)
Fortunato, S.: Community detection in Graphs. Physics Reports 486, 75–174 (2010)
Erdos, P., Renyi, A.: On random graphs I. Publicationes Mathematicae 6, 290–297 (1959)
Condon, A., Karp, R.: Algorithms for graph partitioning on the planted partition model. Rand. Struc. Algor. 18, 116–140 (2001)
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
Stanoev, A., Trpevski, I., Kocarev, L. (2011). An Agglomerative Clustering Technique Based on a Global Similarity Metric. In: Gusev, M., Mitrevski, P. (eds) ICT Innovations 2010. ICT Innovations 2010. Communications in Computer and Information Science, vol 83. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19325-5_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-19325-5_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19324-8
Online ISBN: 978-3-642-19325-5
eBook Packages: Computer ScienceComputer Science (R0)