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Eigenvalues of Random Power law Graphs
Fan Chung, Linyuan Lu, and Van Vu
Department of Mathematics, University of California, San Diego, La Jolla, CA 92093, USA
{fan, vanvu}@ucsd.edu, llu@math.ucsd.edu
Annals of Combinatorics 7 (1) p.21-33 March, 2003
AMS Subject Classification: 05C80
Many graphs arising in various information networks exhibit the ``power law'' behavior --- the number of vertices of degree k is proportional to for some positive . We show that if , the largest eigenvalue of a random power law graph is almost surely where m is the maximum degree. Moreover, the k largest eigenvalues of a random power law graph with exponent have power law distribution with exponent 2-1 if the maximum degree is sufficiently large, where k is a function depending on , m and d, the average degree. When 2 < < 2.5, the largest eigenvalue is heavily concentrated at for some constant c depending on and the average degree. This result follows from a more general theorem which shows that the largest eigenvalue of a random graph with a given expected degree sequence is determined by m, the maximum degree, and d, the weighted average of the squares of the expected degrees. We show that the k-th largest eigenvalue is almost surely where is the k-th largest expected degree provided is large enough. These results have implications on the usage of spectral techniques in many areas related to pattern detection and information retrieval.
Keywords: random graphs, power law, eigenvalues


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