Recently, an abundant work has focused on generating synthetic datasets for graph-oriented benchmarks. The motivation behind this trend is the lack of real and large scale graphs, reflecting the fact that available datasets are either inaccessible because of confidentiality agreements or does not t the required scale and characteristics. However, attempting to mimic real-world graphs imposes signficant challenges since graphs can be used in a myriad of application domains which implies a diversity in graph structures and topologies. Thus, a graph generator should have tunable parameters giving practitioners the flexibility to generate graphs satisfying their own requirements such as clustering coefficients, degree distribution, diameter, community distribution, etc.
While graph generators have extensively focused on static graphs, a new interest focusing on the generation of temporal graphs have recently emerged.

RTGen is temporal graph generation method which generates a sequence of temporal graph snapshot by computing each snapshot from the previous one.