  Property graphs can be used to represent heterogeneous networks with labeled
(attributed) vertices and edges. Given a property graph, simulating another
graph with same or greater size with the same statistical properties with
respect to the labels and connectivity is critical for privacy preservation and
benchmarking purposes. In this work we tackle the problem of capturing the
statistical dependence of the edge connectivity on the vertex labels and using
the same distribution to regenerate property graphs of the same or expanded
size in a scalable manner. However, accurate simulation becomes a challenge
when the attributes do not completely explain the network structure. We propose
the Property Graph Model (PGM) approach that uses a label augmentation strategy
to mitigate the problem and preserve the vertex label and the edge connectivity
distributions as well as their correlation, while also replicating the degree
distribution. Our proposed algorithm is scalable with a linear complexity in
the number of edges in the target graph. We illustrate the efficacy of the PGM
approach in regenerating and expanding the datasets by leveraging two distinct
illustrations. Our open-source implementation is available on GitHub.
