Abstract
A fundamental problem in social networking and computing is the community finding problem that can be used in a lot of social networks’ applications. In this paper, we propose an algorithm that finds the entire community structure of a network, based on interactions between neighboring nodes (distributed method) and on an unsupervised centralized clustering algorithm. Experimental results and comparisons with another method found in the literature are presented for a variety of benchmark graphs with known community structure, derived by varying a number of graph parameters. The experimental results demonstrate the high performance of the proposed algorithm to detect communities.
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
Dabek, F., Cox, R., Kaashoek, F., Morris, R.: Vivaldi: A decentralized network coordinate system. In: Proceedings of the ACM SIGCOMM 2004 Conference (August 2004)
Flake, G.W., Lawrence, S., Giles, C.L., Coetzee, F.M.: Self organization and identification of web communities. IEEE Computer 35, 66–71 (2002)
Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proceedings of the National Academy of Sciences of the United States of America 99(12), 7821–7826 (2002)
Katsaros, D., Pallis, G., Stamos, K., Vakali, A., Sidiropoulos, A., Manolopoulos, Y.: CDNs content outsourcing via generalized communities. IEEE Transactions on Knowledge and Data Engineering 21, 137–151 (2009)
Lancichinetti, A., Fortunato, S.: Community detection algorithms: a comparative analysis. Physical Review EÂ 80 (5 Pt 2), 56117 (2009)
Lancichinetti, A., Fortunato, S., Kertész, J.: Detecting the overlap** and hierarchical community structure in complex networks. New Journal of Physics 11(3), 33015 (2009)
Leskovec, J.: Snap: Stanford network analysis package
MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proc. of the fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297 (1967)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Physical Review EÂ 69(2), 26113 (2004)
Papadopoulos, S., Skusa, A., Vakali, A., Kompatsiaris, Y., Wagner, N.: Bridge bounding: A local approach for efficient community discovery in complex networks. Technical Report ar**v:0902.0871 (February 2009)
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
Papadakis, H., Panagiotakis, C., Fragopoulou, P. (2011). Local Community Finding Using Synthetic Coordinates. In: Park, J.J., Yang, L.T., Lee, C. (eds) Future Information Technology. Communications in Computer and Information Science, vol 185. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22309-9_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-22309-9_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22308-2
Online ISBN: 978-3-642-22309-9
eBook Packages: Computer ScienceComputer Science (R0)