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- 198 creator alessandra-sala.
- 198 creator ben-zhao.
- 198 creator christo-wilson.
- 198 creator haitao-zheng.
- 198 creator lili-cao.
- 198 creator robert-zablit.
- 198 type InProceedings.
- 198 label "Measurement-Calibrated Graph Models for Social Network Experiments".
- 198 sameAs 198.
- 198 abstract "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. Since gathering real graph datasets through direct measurements is costly in time and resources, researchers are anonymizing and distributing valuable datasets to the community. However, performing experiments using shared real datasets faces three significant disadvantages: concerns that graphs can be de-anonymized to reveal private information, increasing costs of distributing large graph datasets, and the fact that the small number of available social graphs limits the statistical confidence in the results. The use of trace-driven 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 trace-driven synthetic graphs using six popular graph models and real social graphs measured 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 of these models 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.".
- 198 hasAuthorList authorList.
- 198 isPartOf proceedings.
- 198 keyword "Social data analysis".
- 198 keyword "analytics".
- 198 title "Measurement-Calibrated Graph Models for Social Network Experiments".