Using Bayesian Network Representations for Effective Sampling from
  Generative Network Models
  Bayesian networks (BNs) are used for inference and sampling by exploiting
conditional independence among random variables. Context specific independence
(CSI) is a property of graphical models where additional independence relations
arise in the context of particular values of random variables (RVs).
Identifying and exploiting CSI properties can simplify inference. Some
generative network models (models that generate social/information network
samples from a network distribution P(G)), with complex interactions among a
set of RVs, can be represented with probabilistic graphical models, in
particular with BNs. In the present work we show one such a case. We discuss
how a mixed Kronecker Product Graph Model can be represented as a BN, and study
its BN properties that can be used for efficient sampling. Specifically, we
show that instead of exhibiting CSI properties, the model has deterministic
context-specific dependence (DCSD). Exploiting this property focuses the
sampling method on a subset of the sampling space that improves efficiency.
