document: Measurement-calibrated graph models for social network experiments Access to realistic , complex graph datasets is critical to research on social networking systems and applications . Simulations on graph data provide critical evaluation of new systems and applications ranging from community detection to spam filtering and social web search . Due to the high time and resource costs of gathering real graph datasets through direct measurements , researchers are anonymizing and sharing a small number of valuable datasets with the community . However , performing experiments using shared real datasets faces three key disadvantages : concerns that graphs can be de-anonymized to reveal private information , increasing costs of distributing large datasets , and that a small number of available social graphs limits the statistical confidence in the results . The use of measurement-calibrated graph models is an attractive alternative to sharing datasets . Researchers can `` fit '' a graph model to a real social graph , extract a set of model parameters , and use them to generate multiple synthetic graphs statistically similar to the original graph . While numerous graph models have been proposed , it is unclear if they can produce synthetic graphs that accurately match the properties of the original graphs . In this paper , we explore the feasibility of measurement-calibrated synthetic graphs using six popular graph models and a variety of real social graphs gathered from the Facebook social network ranging from 30,000 to 3 million edges . We find that two models consistently produce synthetic graphs with common graph metric values similar to those of the original graphs . However , only one produces high fidelity results in our application-level benchmarks . While this shows that graph models can produce realistic synthetic graphs , it also highlights the fact that current graph metrics remain incomplete , and some applications expose graph properties that do not map to existing metrics .
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