论文标题
HyperGraph人工基准用于社区检测（H-ABCD）
Hypergraph Artificial Benchmark for Community Detection (h-ABCD)
论文作者
论文摘要
社区检测（ABCD）图的人工基准是最近引入的随机图模型，具有社区结构和幂律分布的学位和社区规模。该模型生成具有与众所周知的LFR ONE相似属性的图形，并且可以调节其主要参数以模仿LFR模型中的混合参数。在本文中，我们介绍了ABCD模型H-ABCD的HyperGraph对应物，该模型H-ABCD会产生随机的超图，并在Power-Law后具有地面真相社区大小和学位的分布。与原始ABCD一样，新型H-ABCD可以产生具有不同级别噪声的超图。更重要的是，该模型是灵活的，并且可以模仿落入一个社区的Hypereadges的任何期望的同质性水平。结果，它可以用作合成的合成操场，用于分析和调整超图形社区检测算法。
The Artificial Benchmark for Community Detection (ABCD) graph is a recently introduced 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 can be tuned to mimic its counterpart in the LFR model, the mixing parameter. In this paper, we introduce hypergraph counterpart of the ABCD model, h-ABCD, which produces random hypergraph with distributions of ground-truth community sizes and degrees following power-law. As in the original ABCD, the new model h-ABCD can produce hypergraphs with various levels of noise. More importantly, the model is flexible and can mimic any desired level of homogeneity of hyperedges that fall into one community. As a result, it can be used as a suitable, synthetic playground for analyzing and tuning hypergraph community detection algorithms.
