1. A method of relational analysis of data, comprising: receiving a data set {{F (j) } j=1 m ,{S (j) } j=1 m ,{R (ij) } i,j=1 m }, comprising a plurality data objects having a plurality of types of data associated with different latent classes, the plurality of data objects being interrelated, at least a first portion of the plurality of data objects having respective data object attributes {Θ (j) } j=1 m , at least a second portion of the plurality of data objects having homogeneous relations {Γ (j) } j=1 m between the respective data object and data objects having the same type, and at least a third portion of the plurality of data objects having heterogeneous relations { (ij) } i,j=1 m between the respective data object and data objects of different types; providing a mixed membership model {Λ (j) } j=1 m , representing the plurality of data objects, comprising, for each respective data object, relationships with other data objects based on the latent classes, and a latent indicator {C (j) } j=1 m having respective latent class membership parameters generated based on a multinomial distribution; automatically optimizing the mixed membership model {Λ (j) } j=1 m by maximizing a likelihood function {tilde over (Ω)}={{{tilde over (Λ)} (j) } j=1 m ,{{tilde over (Θ)} (j) } j=1 m ,{{tilde over (Γ)} (j) } j=1 m ,{ (ij) } i,j=1 m }}, which is initialized, and the posterior Pr({C (j) }|F (j) } j=1 m ,{S (j) } j=1 m ,{R (ij) } i,j=1 m ,{tilde over (Ω)}) computed using a Monte Carlo approach, to estimate unknown parameters of a joint probability distribution matrix over the latent indicators {C (j) } j=1 m of the plurality of data objects, and observations of the data object attributes {Θ (j) } j=1 m , the homogeneous relations {Γ (j) } j=1 m between the respective data object and data objects having the same type, and the heterogeneous relations { (ij) } i,j=1 m between the respective data object and data objects of different types; and representing in a memory the optimized mixed membership model {Λ (j) } j=1 m .