  The k-core decomposition of a network has thus far mainly served as a
powerful tool for the empirical study of complex networks. We now propose its
explicit integration in a theoretical model. We introduce a Hard-core Random
Network model that generates maximally random networks with arbitrary degree
distribution and arbitrary k-core structure. We then solve exactly the bond
percolation problem on the HRN model and produce fast and precise analytical
estimates for the corresponding real networks. Extensive comparison with
selected databases reveals that our approach performs better than existing
models, while requiring less input information.
