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Towards the Automated Generation of Consistent, Diverse, Scalable and Realistic Graph Models
[chapter]
2018
Lecture Notes in Computer Science
Automated model generation can be highly beneficial for various application scenarios including software tool certification, validation of cyber-physical systems or benchmarking graph databases to avoid tedious manual synthesis of models. In the paper, we present a long-term research challenge how to generate graph models specific to a domain which are consistent, diverse, scalable and realistic at the same time. We provide foundations for a class of model generators along a refinement relation
doi:10.1007/978-3-319-75396-6_16
fatcat:n7yqyhj35bhitikk2hl7fzd37a