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.

翻译：在本文中,我们调查具有内在社区结构的合成随机图形模型的特性和性能,这些模型对于评估和调整不受自然监督的社区检测算法十分重要。我们建议多读实施 ABCD(社区检测人材基准)图形生成器。我们讨论算法的实施细节,并将其与先前已有的ABCD模型的顺序版本以及标准和广泛使用LFR(Lancichinetti-Fortunato-Radichi)生成器的平行实施进行比较。我们显示ABCDE比NetworKit提供的平行实施LFR速度和规模要快十倍以上。此外,ABCD生成的算法不仅速度更快,而且随机性图与原始LFR算法生成的相类似,而同时实施的NetworKit(LFR)生成的图则明显不同。