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Abstract The Artificial Benchmark for Community Detection graph (ABCD) is a random graph model with community structure and powerlaw distribution for both degrees and community sizes. The model generates graphs with similar properties as the well-known LFR one, and its main parameter ξ can be tuned to mimic its counterpart in the LFR model, the mixing parameter μ. In this paper, we extend the ABCD model to include potential outliers. We perform some exploratory experiments on both the new ABCD+o model as well as a real-world network to show that outliers posses some desired, distinguishable properties.
Kamiński et al. (Tue,) studied this question.