  In this work we propose R-GPM, a parallel computing framework for graph
pattern mining (GPM) through a user-defined subgraph relation. More
specifically, we enable the computation of statistics of patterns through their
subgraph classes, generalizing traditional GPM methods. R-GPM provides
efficient estimators for these statistics by employing a MCMC sampling
algorithm combined with several optimizations. We provide both theoretical
guarantees and empirical evaluations of our estimators in application scenarios
such as stochastic optimization of deep high-order graph neural network models
and pattern (motif) counting. We also propose and evaluate optimizations that
enable improvements of our estimators accuracy, while reducing their
computational costs in up to 3-orders-of-magnitude. Finally,we show that R-GPM
is scalable, providing near-linear speedups on 44 cores in all of our tests.
