Artificial benchmark for community detection with outliers (ABCD+o)

The A rtificial B enchmark for C ommunity D etection graph ( ABCD ) is a random graph model with community structure and power-law 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 pose some distinguishable properties. This ensures that our new model may serve as a benchmark of outlier detection algorithms.