Artificial Benchmark for Community Detection with Outliers (ABCD+o)
This work addresses the problem of modeling and detecting outliers in community detection for network analysis, but it is incremental as it builds upon an existing benchmark.
The authors extended the ABCD random graph model to include outliers, creating ABCD+o, and conducted exploratory experiments to demonstrate that outliers in this model and a real-world network exhibit distinguishable properties.
The Artificial Benchmark for Community Detection 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 possess some desired, distinguishable properties.