In this paper a novel method is described for representation and classification of target by random graphs. A target is represented in terms of set primitives that jointly represent a random graph structure. Random graph is a graph structure with randomly varying vertex and arc attribute values. Random graphs and their statistical and matrix representations are useful when one encounters the problem of classifying signatures of partially occluded targets. We present a number of observations that spectra of random graphs of partially occluded and non-occluded target signatures are related through an interlacing rule and the correlations of their Laplacians lead to robust classification.