We analyze complex networks under the random matrix theory framework. Particularly, we show that Δ3 statistics, which gives information about the long-range correlations among eigenvalues, provides a measure of randomness in networks. As networks deviate from the regular structure, Δ3 follows the random matrix prediction of logarithmic behavior (i.e., ) for longer scale.