The hyperbolic random graph model (HRG) has proven useful in the analysis of scale-free networks, which are ubiquitous in many fields, from social network analysis to biology. However, working with this model is algorithmically and conceptually challenging because of the nature of the distances in the hyperbolic plane. In this paper, we propose a discrete variant of the HRG model where nodes are mapped to the vertices of a triangulation; our algorithms allow us to work with this model in a simple yet efficient way. We present experimental results conducted on networks, both real-world and simulated, to evaluate the practical benefits of DHRG in comparison to the HRG model.
翻译:双曲随机图表模型(HRG)在分析从社会网络分析到生物学等许多领域普遍存在的无规模网络方面被证明是有用的。然而,由于双曲平面距离的性质,与这一模型合作在逻辑上和概念上具有挑战性。在本文中,我们提议了一个离散的HRG模型变量,将节点映射到三角的顶部;我们的算法使我们能够以简单而有效的方式与这一模型合作。我们介绍了在现实世界和模拟的网络上进行的实验结果,以评估DHRG相对于HRG模型的实际好处。