Lifted Message Passing for the Generalized Belief Propagation
  We introduce the lifted Generalized Belief Propagation (GBP) message passing
algorithm, for the computation of sum-product queries in Probabilistic
Relational Models (e.g. Markov logic network). The algorithm forms a compact
region graph and establishes a modified version of message passing, which
mimics the GBP behavior in a corresponding ground model. The compact graph is
obtained by exploiting a graphical representation of clusters, which reduces
cluster symmetry detection to isomorphism tests on small local graphs. The
framework is thus capable of handling complex models, while remaining
domain-size independent.
