Properties and Performance of the ABCDe Random Graph Model with Community Structure
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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 a new implementation of the ABCD graph generator, ABCDe, that uses multiple-threading. We discuss the implementation details of the algorithm as well as 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 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.
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