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Property graphs can be used to represent heterogeneous networks with labeled(attributed) vertices and edges. Given a property graph, simulating anothergraph with same or greater size with the same statistical properties withrespect to the labels and connectivity is critical for privacy preservation andbenchmarking purposes. In this work we tackle the problem of capturing thestatistical dependence of the edge connectivity on the vertex labels and usingthe same distribution to regenerate property graphs of the same or expandedsize in a scalable manner. However, accurate simulation becomes a challengewhen the attributes do not completely explain the network structure. We proposethe Property Graph Model (PGM) approach that uses a label augmentation strategyto mitigate the problem and preserve the vertex label and the edge connectivitydistributions as well as their correlation, while also replicating the degreedistribution. Our proposed algorithm is scalable with a linear complexity inthe number of edges in the target graph. We illustrate the efficacy of the PGMapproach in regenerating and expanding the datasets by leveraging two distinctillustrations. Our open-source implementation is available on GitHub.

When Labels Fall Short: Property Graph Simulation via Blending of Network Structure and Vertex Attributes