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Poisson convergence and semi-induced properties of random graphs

Published online by Cambridge University Press:  24 October 2008

Michał Karoński
Affiliation:
Institute of Mathematics, Adam Mickiewicz University, Poznań, Poland
Andrzej Ruciński
Affiliation:
Institute of Mathematics, Adam Mickiewicz University, Poznań, Poland

Extract

Barbour [l] invented an ingenious method of establishing the asymptotic distribution of the number X of specified subgraphs of a random graph. The novelty of his method relies on using the first two moments of X only, despite the traditional method of moments that involves all moments of X (compare [8, 10, 11, 14]). He also adjusted that new method for counting isolated trees of a given size in a random graph. (For further applications of Barbour's method see [4] and [10].) The main goal of this paper is to show how this method can be extended to a general setting that enables us to derive asymptotic distributions of subsets of vertices of a random graph with various properties.

Type
Research Article
Copyright
Copyright © Cambridge Philosophical Society 1987

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References

REFERENCES

[1]Barbour, A. D.. Poisson convergence and random graphs. Math. Proc. Cambridge Philos. Soc. 92 (1982), 349359.CrossRefGoogle Scholar
[2]Barbour, A. D. and Eagleson, G. K.. Poisson approximation for some statistics based on exchangeable trials. Adv. in Appl. Probab. 15 (1982), 585600.Google Scholar
[3]BollobáS, B.. Vertices of given degree in a random graph. J. Graph Theory. 6 (1982), 147155.Google Scholar
[4]Bollobás, B.. Random Graphs (Academic Press, 1985).Google Scholar
[5]Burtin, Yu D.. On extreme metric characteristics of a random graph. I. Asymptotic estimates. Theory Probab. Appl. 19, no. 4 (1974), 711725.Google Scholar
[6]Burtin, Yu D.. On extreme metric characteristics of a random graph. II. Limit distributions. Theory Probab. Appl. 20, no. 1 (1975), 83101.CrossRefGoogle Scholar
[7]Chow, Y. and Teicher, H.. Probability Theory (Springer-Verlag, 1978).CrossRefGoogle Scholar
[8]Erdös, P. and Rényi, A.. On the evolution of random graphs. Publ. Math. Inst. Hungar. Acad. Sci. 5 (1960), 1761.Google Scholar
[9]Hakimullin, E.. On the valencies of degrees in a random graph. Probabilistic Processes no. 282 (1979), 136140.Google Scholar
[10]Karoński, M.. Balanced Subgraphs of Large Random Graphs (Adam Mickiewicz University Press, Poznań, 1984).Google Scholar
[11]Karoński, M. and Ruciński, A.. On the number of strictly balanced subgraphs of a random graph. In Graph Theory, Łagów, 1981, Lecture Notes in Math. 1018 (Springer-Verlag, 1983), 7983.Google Scholar
[12]Kesten, H.. Percolation Theory for Mathematics (Birkhäuser, 1982).CrossRefGoogle Scholar
[13]Palka, Z.. On the number of vertices of a given degree in a random graph. J. Graph Theory 8 (1984), 167170.Google Scholar
[14]Schükger, K.. Limit theorems for complete subgraphs of random graphs. Period. Math. Hungar. 10 (1979), 4753.Google Scholar