Abstract
Uncovering the underlying community structure of networks modelling real-world complex systems is essential way to gain insight both into their structure and their functional organization. Of all the definitions of community proposed by researchers, we focused on the k-clique community definition as we believe it best catches the characteristics of many real networks. Currently, extracting k-clique communities using the methods available in the literature requires a formidable amount of computational load and memory resources. In this paper we propose a new parallel method that has proved its capability in extracting k-clique communities efficiently and effectively from some real-world complex networks for which these communities had never been detected before. This innovative method is much less resource intensive than Clique Percolation Method and experimental results show it is always at least an order of magnitude faster. In addition, tests run on parallel architectures show a noticeable speedup factor, in some cases linear with the number of cores.
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- 1.
A connected group of one or more IP prefixes run by one or more network operators that has a single and clearly defined routing policy.
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(2x) Intel E7600 CPU @ 3.06 GHz; (4x) 2 GB RAM modules @ 1067 MHz; Mac OS X 10.6.4 operating system, Darwin 10.4.0 kernel
- 3.
(4x) Intel E7540 CPU @ 2 GHz; (16x) 4 GB RAM modules @ 1067 MHz; GNU/Linux operating system; Linux 2.6.35.22 kernel
References
Bomze, I.M., Budinich, M., Pardalos, P.M., Pelillo, M.: The maximum clique problem. In: Du, D.-Z., Pardalos, P.M. (eds.) Handbook of Combinatorial Optimization. Kluwer Academic Publishers, Boston (1999)
Eppstein, D., Galil, Z., Italiano, G.F.: Dynamic graph algorithms. In: Atallah, M.J. (ed.) Algorithms and Theory of Computation Handbook. Purdue University, CRC Press, West Lafayette (1998)
Everett, M.G., Borgatti, S.P.: Analyzing clique overlap. Connections 21(1), 49–61 (1998)
Gregori, E., Lenzini, L., Orsini, C.: k-clique communities in the internet AS-level topology graph. In: SIMPLEX 2011 (2011)
Kumpula, J.M., Kivelä, M., Kaski, K., Saramäki, J.: Sequential algorithm for fast clique percolation. Phys. Rev. E 78(2), 026109 (2008)
Palla, G., Derenyi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043), 814–818 (2005)
Tarjan, R.E., van Leeuwen, J.: Worst-case analysis of set union algorithms. J. ACM 31, 245–281 (1984)
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© 2011 Springer-Verlag London Limited
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Gregori, E., Lenzini, L., Mainardi, S., Orsini, C. (2011). FLIP-CPM: A Parallel Community Detection Method. In: Gelenbe, E., Lent, R., Sakellari, G. (eds) Computer and Information Sciences II. Springer, London. https://doi.org/10.1007/978-1-4471-2155-8_31
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DOI: https://doi.org/10.1007/978-1-4471-2155-8_31
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Online ISBN: 978-1-4471-2155-8
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