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 pose some distinguishable properties. This ensures that our new model may serve as a benchmark of outlier detection algorithms.

A rtificial B enchmark for Community D etection graph ( ABCD ) 是一个随机图模型，具有社区结构和度数和社区大小的幂律分布。该模型生成的图具有与众所周知的LFR相似的属性，并且可以调整其主要参数\(\xi\)以模仿LFR模型中的对应参数，即混合参数\(\mu\)。在本文中，我们扩展了ABCD模型以包含潜在的异常值。我们对新的ABCD+o进行了一些探索性实验模型以及真实世界的网络，以表明异常值具有一些可区分的属性。这确保了我们的新模型可以作为异常值检测算法的基准。