Hostname: page-component-586b7cd67f-gb8f7 Total loading time: 0 Render date: 2024-11-24T12:10:19.231Z Has data issue: false hasContentIssue false

Poisson Approximation of the Number of Cliques in Random Intersection Graphs

Published online by Cambridge University Press:  14 July 2016

Katarzyna Rybarczyk*
Affiliation:
Adam Mickiewicz University
Dudley Stark*
Affiliation:
Queen Mary, University of London
*
Postal address: Faculty of Mathematics and Computer Science, Adam Mickiewicz University, ul. Umultowska 87, 61-614 Poznań, Poland.
∗∗Postal address: School of Mathematical Sciences, Queen Mary, University of London, London E1 4NS, UK. Email address: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

A random intersection graph G(n, m, p) is defined on a set V of n vertices. There is an auxiliary set W consisting of m objects, and each vertex vV is assigned a random subset of objects WvW such that wWv with probability p, independently for all vV and all wW. Given two vertices v1, v2V, we set v1v2 if and only if Wv1Wv2 ≠ ∅. We use Stein's method to obtain an upper bound on the total variation distance between the distribution of the number of h-cliques in G(n, m, p) and a related Poisson distribution for any fixed integer h.

Type
Research Article
Copyright
Copyright © Applied Probability Trust 2010 

References

[1] Behrisch, M. (2007). Component evolution in random intersection graphs. Electron. J. Combinatorics 14, R17, 12 pp.CrossRefGoogle Scholar
[2] Blackburn, S. R. and Gerke, S. (2009). Connectivity of the uniform random intersection graph. Discrete Math. 309, 51305140.CrossRefGoogle Scholar
[3] Blanchard, Ph., Krueger, A., Krueger, T. and Martin, P. (2005). The epidemics of corruption. Preprint. Available at http://arxiv.org/abs/physics/0505031v1.Google Scholar
[4] Bloznelis, M., Jaworski, J. and Rybarczyk, K. (2009). Component evolution in a secure wireless sensor network. Networks 53, 1926.CrossRefGoogle Scholar
[5] Britton, T., Deijfen, M., Lagerås, A. N. and Lindholm, M. (2008). Epidemics on random graphs with tunable clustering. J. Appl Prob. 45, 743756.CrossRefGoogle Scholar
[6] Deijfen, M. and Kets, W. (2009). Random intersection graphs with tunable degree distribution and clustering. Prob. Eng. Inf. Sci. 23, 661674.CrossRefGoogle Scholar
[7] Di Pietro, R. et al. (2006). Sensor networks that are provably resilient. In Proc. IEEE Internat. Conf. on Security and Privacy in Communication Networks (SECURECOMM 2006), Baltimore.Google Scholar
[8] Fill, J. A., Scheinerman, E. R. and Singer-Cohen, K. B. (2000). Random intersection graphs when m=ω(n): an equivalence theorem relating the evolution of the G(n,m,p) and G(n,p) models. Random Structures Algorithms 16, 156176.3.0.CO;2-H>CrossRefGoogle Scholar
[9] Godehardt, E. and Jaworski, J. (2002). Two models of random intersection graphs for classification. In Exploratory Data Analysis in Empirical Research, eds Opitz, O. and Schwaiger, M., Springer, Berlin, pp. 6781.Google Scholar
[10] Godehardt, E., Jaworski, J. and Rybarczyk, K. (2007). Random intersection graphs and classification. In Advances in Data Analysis, eds Decker, R. and Lenz, H.-J., Springer, Berlin, pp. 6774.CrossRefGoogle Scholar
[11] Godehardt, E., Jaworski, J. and Rybarczyk, K. (2010). Isolated vertices in random intersection graphs. In Advances in Data Analysis, Data Handling and Business Intelligence, eds Fink, A. et al., Springer, Berlin, pp. 135145.Google Scholar
[12] Janson, S., Łuczak, T. and Ruciński, A. (2000). Random Graphs. Wiley-Interscience, New York.CrossRefGoogle Scholar
[13] Karoński, M., Scheinerman, E. R. and Singer-Cohen, K. B. (1999). On random intersection graphs: the subgraph problem. Combinatorics Prob. Comput. 8, 131159.CrossRefGoogle Scholar
[14] Marchette, D. J. (2004). Random Graphs for Statistical Pattern Recognition. John Wiley, Hoboken, NJ.CrossRefGoogle Scholar
[15] Newman, M. E. J., Strogatz, S. H. and Watts, D. J. (2001). Random graphs with arbitrary degree distributions and their applications. Phys. Rev. E 64, 026118.CrossRefGoogle ScholarPubMed
[16] Newman, M. E. J., Watts, D. J. and Strogatz, S. H. (2002). Random graph models of social networks. Proc. Nat. Acad. Sci. USA 99, 25662572.CrossRefGoogle ScholarPubMed