In this paper, we present a novel technique to automatically synthesize consistent,
diverse and structurally realistic domain-specific graph models. A graph model is
(1) consistent if it is metamodel-compliant and it satisfies the well-formedness constraints
of the domain; (2) it is diverse if local neighborhoods of nodes are highly different;
and (1) it is structurally realistic if a synthetic graph is at a close distance to
a representative real model according to various graph metrics used in network science,
databases or software engineering. Our approach grows models by model extension operators
using a hill-climbing strategy in a way that (A) ensures that there are no constraint
violation in the models (for consistency reasons), while (B) more realistic candidates
are selected to minimize a target metric value (wrt. the representative real model).
We evaluate the effectiveness of the approach for generating realistic models using
multiple metrics for guidance heuristics and compared to other model generators in
the context of three case studies with a large set of real human models. We also highlight
that our technique is able to generate a diverse set of models, which is a requirement
in many testing scenarios.
