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Comparative analysis of two discretizations of Ricci curvature for complex networks

机译:复杂网络的Ricci曲率的两种离散化的比较分析

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摘要

We have performed an empirical comparison of two distinct notions of discrete Ricci curvature for graphs or networks, namely, the Forman-Ricci curvature and Ollivier-Ricci curvature. Importantly, these two discretizations of the Ricci curvature were developed based on different properties of the classical smooth notion, and thus, the two notions shed light on different aspects of network structure and behavior. Nevertheless, our extensive computational analysis in a wide range of both model and real-world networks shows that the two discretizations of Ricci curvature are highly correlated in many networks. Moreover, we show that if one considers the augmented Forman-Ricci curvature which also accounts for the two-dimensional simplicial complexes arising in graphs, the observed correlation between the two discretizations is even higher, especially, in real networks. Besides the potential theoretical implications of these observations, the close relationship between the two discretizations has practical implications whereby Forman-Ricci curvature can be employed in place of Ollivier-Ricci curvature for faster computation in larger real-world networks whenever coarse analysis suffices.
机译:我们对图形或网络的离散Ricci曲率的两个不同概念(即Forman-Ricci曲率和Ollivier-Ricci曲率)进行了实证比较。重要的是,Ricci曲率的这两个离散化是基于经典平滑概念的不同属性而开发的,因此,这两个概念阐明了网络结构和行为的不同方面。尽管如此,我们在大量模型网络和实际网络中进行的大量计算分析表明,在许多网络中,Ricci曲率的两个离散化高度相关。此外,我们表明,如果考虑增加的Forman-Ricci曲率,这也说明了图中出现的二维单纯形复数,则观察到的两个离散化之间的相关性甚至更高,尤其是在实际网络中。除了这些观察结果的潜在理论含义外,两次离散之间的紧密关系还具有实际意义,因此只要粗略分析就足够了,就可以在较大的实际网络中使用Forman-Ricci曲率代替Ollivier-Ricci曲率来更快地进行计算。

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