  Sum-Product Networks (SPNs) are a class of expressive yet tractable
hierarchical graphical models. LearnSPN is a structure learning algorithm for
SPNs that uses hierarchical co-clustering to simultaneously identifying similar
entities and similar features. The original LearnSPN algorithm assumes that all
the variables are discrete and there is no missing data. We introduce a
practical, simplified version of LearnSPN, MiniSPN, that runs faster and can
handle missing data and heterogeneous features common in real applications. We
demonstrate the performance of MiniSPN on standard benchmark datasets and on
two datasets from Google's Knowledge Graph exhibiting high missingness rates
and a mix of discrete and continuous features.
