Detecting randomwalks hidden in noise: Phase transition on large graphs
We consider the problem of distinguishing between two hypotheses: that a sequence of signals on a large graph consists entirely of noise, or that it contains a realization of a random walk buried in the noise. The problem of computing the error exponent of the optimal detector is simple to formulate, but exhibits deep connections to problems known to be difficult, such as computing Lyapunov exponents of products of random matrices and the free entropy density of statistical mechanical systems. We describe these connections, and define an algorithm that efficiently computes the error exponent of the Neyman-Pearson detector. We also derive a closed-form formula, derived from a statistical mechanics-based approximation, for the error exponent on an arbitrary graph of large size. The derivation of this formula is not entirely rigorous, but it closely matches the empirical results in all our experiments. This formula explains a phase transition phenomenon in the error exponent: below a threshold SNR, the error exponent is nearly constant and near zero, indicating poor performance; above the threshold, there is rapid improvement in performance as the SNR increases. The location of the phase transition depends on the entropy rate of the random walk.