Spectral clustering is a widely used method for community detection in\nnetworks. We focus on a semi-supervised community detection scenario in the\nPartially Labeled Stochastic Block Model (PL-SBM) with two balanced\ncommunities, where a fixed portion of labels is known. Our approach leverages\nrandom walks in which the revealed nodes in each community act as absorbing\nstates. By analyzing the quasi-stationary distributions associated with these\nrandom walks, we construct a classifier that distinguishes the two communities\nby examining differences in the associated eigenvectors. We establish upper and\nlower bounds on the error rate for a broad class of quasi-stationary\nalgorithms, encompassing both spectral and voting-based approaches. In\nparticular, we prove that this class of algorithms can achieve the optimal\nerror rate in the connected regime. We further demonstrate empirically that our\nquasi-stationary approach improves performance on both real-world and simulated\ndatasets.\n