Artificial Benchmark for Community Detection with Outliers (ABCD+o)

• URL: http://arxiv.org/abs/2301.05749v2
• Date: Mon, 12 Jun 2023 18:43:35 GMT
• Title: Artificial Benchmark for Community Detection with Outliers (ABCD+o)
• Authors: Bogumi{\l} Kami\'nski, Pawe{\l} Pra{\l}at, Fran\c{c}ois Th\'eberge
• Abstract summary: 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.
• Score: 5.8010446129208155
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 $\xi$ can be tuned to mimic its counterpart in the LFR model, the mixing parameter $\mu$. 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.

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