1. A method of relational analysis of data, comprising: receiving a data set comprising a plurality of interrelated data objects having at least one type of data associated with different latent classes, having at least one of respective data object attributes, homogeneous relations between the respective data object and data objects having the same type, and heterogeneous relations between the respective data object and data objects having different types; providing an unsupervised mixed membership relational clustering model for determining a clustering of the plurality of data objects, based on at least respective relationships with other data objects dependent on the different latent classes; generating latent indicators for the plurality of data objects, comprising respective latent class membership parameters based on a predetermined distribution, wherein the latent indicators have respective latent class membership parameters generated based on a distribution selected from the group consisting of: a multinomial distribution; a Bernoulli distribution; a normal distribution; and an exponential distribution; automatically optimizing the mixed membership relational clustering model by maximizing a likelihood function to estimate unknown parameters of a joint probability distribution matrix over the latent indicators of the plurality of data objects, and observations of the data object attributes; using the optimized mixed membership relational clustering model to partition an arbitrarily complex graph involving at least the data object attributes, the homogeneous relations and the heterogeneous relations; and representing in a memory the partitioned graph.
3. The method according to claim 1 , wherein the latent indicators have respective latent class membership parameters generated based on a multinomial distribution.