Graph Theoretic and Spectral Analysis of Enron Email Data
Computational & Mathematical Organization Theory
Systematic topology analysis and generation using degree correlations
Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
Scalable modeling of real graphs using Kronecker multiplication
Proceedings of the 24th international conference on Machine learning
Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
User interactions in social networks and their implications
Proceedings of the 4th ACM European conference on Computer systems
A measurement-driven analysis of information propagation in the flickr social network
Proceedings of the 18th international conference on World wide web
Large Online Social Footprints--An Emerging Threat
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 03
On the leakage of personally identifiable information via online social networks
ACM SIGCOMM Computer Communication Review
A case for P2P infrastructure for social networks - opportunities & challenges
WONS'09 Proceedings of the Sixth international conference on Wireless On-Demand Network Systems and Services
Measurement-calibrated graph models for social network experiments
Proceedings of the 19th international conference on World wide web
Re-Socializing Online Social Networks
GREENCOM-CPSCOM '10 Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing
Safebook: A privacy-preserving online social network leveraging on real-life trust
IEEE Communications Magazine
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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.