Hypergraph Artificial Benchmark for Community Detection (h–ABCD)

IF 2.2 4区数学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Journal of complex networks Pub Date : 2023-06-23 DOI:10.1093/comnet/cnad028

Bogumił Kamiński, Paweł Prałat, François Théberge

引用次数: 0

Abstract

Abstract 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 Lancichinetti, Fortunato, Radicchi (LFR) one, and its main parameter ξ can be tuned to mimic its counterpart in the LFR model, the mixing parameter μ. In this article, we introduce hypergraph counterpart of the ABCD model, h–ABCD, which also 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. [Received on 22 October 2022; editorial decision on 18 July 2023; accepted on 19 July 2023]

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社区检测的超图人工基准

ABCD (Artificial Benchmark for Community Detection)图是近年来提出的一种随机图模型，它具有社团结构和社团大小的幂律分布。该模型生成的图与著名的Lancichinetti, Fortunato, Radicchi (LFR)模型具有相似的性质，并且其主要参数ξ可以被调整以模拟LFR模型中的对应参数，即混合参数μ。在本文中，我们引入了ABCD模型的对应超图h-ABCD, h-ABCD也产生了基于真值社区大小和度服从幂律分布的随机超图。与原来的ABCD一样，新模型h-ABCD可以产生具有不同程度噪声的超图。更重要的是，该模型是灵活的，可以模拟属于一个社区的任何期望级别的超边缘同质性。因此，它可以作为一个合适的综合平台，用于分析和调优超图社区检测算法。[2022年10月22日收到;2023年7月18日的编辑决定;于2023年7月19日接受]

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of complex networks MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-

CiteScore

4.20

自引率

9.50%

发文量

期刊介绍： Journal of Complex Networks publishes original articles and reviews with a significant contribution to the analysis and understanding of complex networks and its applications in diverse fields. Complex networks are loosely defined as networks with nontrivial topology and dynamics, which appear as the skeletons of complex systems in the real-world. The journal covers everything from the basic mathematical, physical and computational principles needed for studying complex networks to their applications leading to predictive models in molecular, biological, ecological, informational, engineering, social, technological and other systems. It includes, but is not limited to, the following topics: - Mathematical and numerical analysis of networks - Network theory and computer sciences - Structural analysis of networks - Dynamics on networks - Physical models on networks - Networks and epidemiology - Social, socio-economic and political networks - Ecological networks - Technological and infrastructural networks - Brain and tissue networks - Biological and molecular networks - Spatial networks - Techno-social networks i.e. online social networks, social networking sites, social media - Other applications of networks - Evolving networks - Multilayer networks - Game theory on networks - Biomedicine related networks - Animal social networks - Climate networks - Cognitive, language and informational network