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Isolated Vertices in Random Intersection Graphs

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Advances in Data Analysis, Data Handling and Business Intelligence

Abstract

For the structure analysis of non-metric data, it is natural to classify objects according to the properties they possess. An effective model to analyze the structure of similarities between objects is the random intersection graph generated by the random bipartite graph with bipartition \((\mathcal{V},\mathcal{W})\), where \(\mathcal{V}\) is a set of objects, \(\mathcal{W}\) is a set of properties, and according to some random procedure, edges join objects with their properties. In the related random intersection graph two vertices are joined by an edge if and only if they represent objects sharing at least s properties. In this paper we study the number of isolated vertices and its convergence to Poisson distribution. We generalize previous results obtained for special cases of the random model and for s = 1, only. Our approach leads us also to some interesting results on dependencies between the appearances of edges in the random intersection graph.

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References

  • Bloznelis, M., Jaworski, J., & Rybarczyk, K. (2009). Component evolution in a secure wireless sensor network. Networks, 53, 19–26.

    Article  MATH  MathSciNet  Google Scholar 

  • Bock, H. H. (1996). Probabilistic models in cluster analysis. Computational Statistics and Data Analysis, 23, 5–28.

    Article  MATH  Google Scholar 

  • Godehardt, E. (1990). Graphs as structural models. Braunschweig: Vieweg.

    Google Scholar 

  • Godehardt, E., & Jaworski, J. (2002). Two models of random intersection graphs for classification. In M. Schwaiger, & O. Opitz (Eds.), Exploratory data analysis in empirical research (pp. 68–81). Berlin Heidelberg New York: Springer.

    Google Scholar 

  • Godehardt, E., Jaworski, J., & Rybarczyk, K. (2007). Random intersection graphs and classification. In R. Decker, & Lenz, H.-J. (Eds.), Advances in data analysis (pp. 67–74). Berlin Heidelberg New York: Springer.

    Google Scholar 

  • Karoński, M., Scheinerman, E. R., & Singer-Cohen, K. B. (1999). On random intersection graphs: The subgraph problem. Combinatorics, Probability and Computing, 8, 131–159.

    Article  MATH  MathSciNet  Google Scholar 

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Acknowledgements

J. Jaworski and K. Rybarczyk acknowledges the support by Ministry of Science and Higher Education, grant N N206 2701 33, 2007–2010. This work also had been supported by the Deutsche Forschungsgesellschaft (grant no. GO 490/13-1, GO 490/15-1).

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Correspondence to Katarzyna Rybarczyk .

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© 2009 Springer-Verlag Berlin Heidelberg

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Godehardt, E., Jaworski, J., Rybarczyk, K. (2009). Isolated Vertices in Random Intersection Graphs. In: Fink, A., Lausen, B., Seidel, W., Ultsch, A. (eds) Advances in Data Analysis, Data Handling and Business Intelligence. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01044-6_12

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