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The model generates graphs with similar properties as the well-known\n                    <jats:bold>LFR<\/jats:bold>\n                    one, and its main parameter\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$\\xi$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mi>\u03be<\/mml:mi>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    can be tuned to mimic its counterpart in the\n                    <jats:bold>LFR<\/jats:bold>\n                    model, the mixing parameter\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$\\mu$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mi>\u03bc<\/mml:mi>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    . In this paper, we extend the\n                    <jats:bold>ABCD<\/jats:bold>\n                    model to include potential outliers. We perform some exploratory experiments on both the new\n                    <jats:bold>ABCD+o<\/jats:bold>\n                    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.\n                  <\/jats:p>","DOI":"10.1007\/s41109-023-00552-9","type":"journal-article","created":{"date-parts":[[2023,5,22]],"date-time":"2023-05-22T05:02:49Z","timestamp":1684731769000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Artificial benchmark for community detection with outliers (ABCD+o)"],"prefix":"10.1007","volume":"8","author":[{"given":"Bogumi\u0142","family":"Kami\u0144ski","sequence":"first","affiliation":[]},{"given":"Pawe\u0142","family":"Pra\u0142at","sequence":"additional","affiliation":[]},{"given":"Fran\u00e7ois","family":"Th\u00e9berge","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,5,22]]},"reference":[{"issue":"3","key":"552_CR1","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1007\/s10618-014-0365-y","volume":"29","author":"L Akoglu","year":"2015","unstructured":"Akoglu L, Tong H, Koutra D (2015) Graph based anomaly detection and description: a survey. 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