14. A method of detection of a community in a network, comprising: automatically optimizing an unsupervised mixed membership relational clustering model based on at least respective relationships between a plurality of interrelated data objects, dependent on different latent classes having respective latent class membership parameters, by maximizing a likelihood function to estimate unknown parameters of a joint probability distribution over latent indicators of the 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, and observations of the plurality of data object attributes; clustering the interrelated plurality of data objects according to the optimized unsupervised mixed membership relational clustering model; wherein the plurality of interrelated data objects comprise a set of social network data objects, and relations comprise social links; and detecting a social community within the social network data objects, based on the clustering.
14. A method of detection of a community in a network, comprising: automatically optimizing an unsupervised mixed membership relational clustering model based on at least respective relationships between a plurality of interrelated data objects, dependent on different latent classes having respective latent class membership parameters, by maximizing a likelihood function to estimate unknown parameters of a joint probability distribution over latent indicators of the 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, and observations of the plurality of data object attributes; clustering the interrelated plurality of data objects according to the optimized unsupervised mixed membership relational clustering model; wherein the plurality of interrelated data objects comprise a set of social network data objects, and relations comprise social links; and detecting a social community within the social network data objects, based on the clustering. 18. The method according to claim 14 , wherein the likelihood function is maximized using a posterior computed using the Gibbs sampler.