| Autor: |
Bogumił Kamiński, Paweł Prałat, François Théberge |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Applied Network Science, Vol 8, Iss 1, Pp 1-22 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2364-8228 |
| DOI: |
10.1007/s41109-023-00552-9 |
| Popis: |
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 $$\xi$$ ξ can be tuned to mimic its counterpart in the LFR model, the mixing parameter $$\mu$$ 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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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