partners: LMU; projects: NESSoS; NoTier cites:0

The evaluation of novel algorithms, protocols, applications, or security attacks in context of Online Social Networks (OSN) necessitates datasets that represent a realistic snapshot of the underlying social graph. As crawling social graphs can become a time and resource consuming task, only a few anonymized datasets exist which are shared among the research community. Besides concerns about de-anonymization attacks on crawled graphs and the fact that such datasets cannot satisfy the statistical confidence in simulation results, more and more secure and privacy-preserving Peer-to-Peer (P2P) OSN architectures emerge that do not facilitate crawling of social graph data at all. In order to evaluate new metrics for OSNs in general, we need social graph models which enable the generation of synthetic datasets. In this paper we present a generic model to synthesize social interaction graphs for both centralized OSNs like Facebook and secure and privacy-preserving P2P OSNs such as Vegas. Our approach accounts for a static component which models relationships and network effects and a dynamic component which models interactions among users. A flexible parameterization schema allows our model to individually influence certain graph characteristics like node degrees, clustering coefficients, and node interactions.