福曼里奇曲率的增量及其在群落探测中的应用

IF 2.6 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Lukas Fesser, Sergio Serrano de Haro Iváñez, Karel Devriendt, Melanie Weber and Renaud Lambiotte
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引用次数: 0

摘要

最近,图上曲率的概念在网络界受到了广泛关注,尤其是奥利维尔-利玛窦曲率(Ollivier-Ricci Curvature,ORC)被用于网络分析中的多项任务,如社群检测。在这项工作中,我们选择了一种不同的方法,研究了福曼(Forman)提出的利玛窦曲率离散化增强方法(AFRC)。我们从经验和理论上研究了它与 ORC 和未增强的 Forman-Ricci 曲率之间的关系。特别是,我们提供的证据表明,AFRC 经常能充分揭示网络结构,可用于社群检测,因此,它是以前基于 ORC 方法的一种计算成本更低的替代方法。我们基于 AFRC 的新型群落检测算法可与基于 ORC 的方法相媲美。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Augmentations of Forman’s Ricci curvature and their applications in community detection
The notion of curvature on graphs has recently gained traction in the networks community, with the Ollivier–Ricci curvature (ORC) in particular being used for several tasks in network analysis, such as community detection. In this work, we choose a different approach and study augmentations of the discretization of the Ricci curvature proposed by Forman (AFRC). We empirically and theoretically investigate its relation to the ORC and the un-augmented Forman–Ricci curvature. In particular, we provide evidence that the AFRC frequently gives sufficient insight into the structure of a network to be used for community detection, and therefore provides a computationally cheaper alternative to previous ORC-based methods. Our novel AFRC-based community detection algorithm is competitive with an ORC-based approach.
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来源期刊
Journal of Physics Complexity
Journal of Physics Complexity Computer Science-Information Systems
CiteScore
4.30
自引率
11.10%
发文量
45
审稿时长
14 weeks
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