As discussed in previous posts, one of the features that makes Datagen more realistic is the fact that the activity volume of the simulated Persons is not uniform, but forms spikes. In this blog entry I want to explain more in depth how this is actually implemented inside of the generator.

This blog entry is about one of the features of DATAGEN that makes it different from other synthetic graph generators that can be found in the literature: the community structure of the graph.

In previous posts (this and this) we briefly introduced the design goals and philosophy behind DATAGEN, the data generator used in LDBC-SNB. In this post, I will explain how to use DATAGEN to generate the necessary datatsets to run LDBC-SNB. Of course, as DATAGEN is continuously under development,  the instructions given in this tutorial might change in the future.

As explained in a previous post, the LDBC Social Network Benchmark (LDBC-SNB) has the objective to provide a realistic yet challenging workload, consisting of a social network and a set of queries. Both have to be realistic, easy to understand and easy to generate. This post has the objective to discuss the main features of DATAGEN, the social network data generator provided by LDBC-SNB, which is an evolution of S3G21.