  In this paper, we investigate properties and performance of synthetic random
graph models with a built-in community structure. Such models are important for
evaluating and tuning community detection algorithms that are unsupervised by
nature. We propose ABCDe, a multi-threaded implementation of the ABCD
(Artificial Benchmark for Community Detection) graph generator. We discuss the
implementation details of the algorithm and compare it with both the previously
available sequential version of the ABCD model and with the parallel
implementation of the standard and extensively used LFR
(Lancichinetti--Fortunato--Radicchi) generator. We show that ABCDe is more than
ten times faster and scales better than the parallel implementation of LFR
provided in NetworKit. Moreover, the algorithm is not only faster but random
graphs generated by ABCD have similar properties to the ones generated by the
original LFR algorithm, while the parallelized NetworKit implementation of LFR
produces graphs that have noticeably different characteristics.
