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Statistics of cycles in large networks

We present a Markov Chain Monte Carlo method for sampling cycle length in large graphs. Cycles are treated as microstates of a system with many degrees of freedom. Cycle length corresponds to energy such that the length histogram is obtained as the density of states from Metropolis sampling. In many growing networks, mean cycle length increases algebraically with system size. The cycle exponent $α$ is characteristic of the local growth rules and not determined by the degree exponent $γ$. For example, $α=0.76(4)$ for the Internet at the Autonomous Systems level.