  We propose networked exponential families to jointly leverage the information
in the topology as well as the attributes (features) of networked data points.
Networked exponential families are a flexible probabilistic model for
heterogeneous datasets with intrinsic network structure. These models can be
learnt efficiently using network Lasso which implicitly pools or clusters the
data points according to the intrinsic network structure and the local
likelihood. The resulting method can be formulated as a non-smooth convex
optimization problem which we solve using a primal-dual splitting method. This
primal-dual method is appealing for big data applications as it can be
implemented as a highly scalable message passing algorithm.
