Graph Generation with Prescribed Feature Constraints

Abstract

In this paper, we study the problem of how to generate synthetic graphs matching various properties of a real social network with two applications, privacy preserving social network publishing and significance testing of network analysis results. We present a simple switching based graph generation approach to generate graphs preserving features of a real graph. We then investigate potential disclosures of sensitive links due to the preserved features. Our algorithms on graph generation with feature range and feature distribution constraints are based on the Metropolis-Hastings sampling. This is of importance for significance testing of network analysis results.