具有地理因素的小世界分析增广图模型

V. Nguyen, C. Martel
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引用次数: 1

摘要

小世界性质,如小直径和聚类,以及幂律性质被广泛认为是大规模现实世界网络的共同特征。最近的研究还注意到两个重要的地理因素发挥了重要作用,特别是在与互联网相关的环境中。这两个是距离偏倚倾向(链接倾向于连接到更近的节点)和局部有界增长的性质。然而,现实世界复杂网络的现有正式模型通常没有充分考虑这些地理因素。我们使用标准增宽图模型(例如Watt和Strogatz的[33]和Kleinberg的[20]模型)描述了一种灵活的方法,并在一个细化模型上提出了重要的初步结果,其中我们专注于小直径特征和上述两个地理因素。我们从一个一般模型开始,其中任意初始节点加权图H被一个通用的“分布规则”τ和H中节点的权重指定的附加随机链接所增强。我们考虑了一个改进的设置,其中初始图H与一个增长有界度量相关联,并且τ具有距离偏差特征,由参数指定如下。基图H具有上下有界的邻域增长,由参数β1, β2 >指定。(这些参数可以被认为是图的维度,例如β1 = 2和β1 = 3对于一个在2D和3D设置中都有节点的图建模设置。)即2β1≤Nu(2r)/Nu(r)≤2β2,其中Nu(r)是距离u的度量距离r内的节点数v: d(u, v)≤r。当我们使用分布τ添加随机链接时,该分布由参数α > 0指定,使得从u到v的链接≠u的概率为∝1/dα(u,v)。我们展示了哪些参数产生小直径图,以及直径如何根据距离偏置参数α与两个有界生长参数β1, β2 > 0之间的关系而变化。特别地,对于大多数连通基图,当α≤β1时,增广图的直径是对数的,当β2≤α 2β2时,增广图的直径是多对数的。这些结果也为设计路由算法的应用提供了有希望的启示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Augmented Graph Models for Small-World Analysis with Geographic Factors
Small-world properties, such as small-diameter and clustering, and the power-law property are widely recognized as common features of large-scale real-world networks. Recent studies also notice two important geographical factors which play a significant role, particularly in Internet related setting. These two are the distance-bias tendency (links tend to connect to closer nodes) and the property of bounded growth in localities. However, existing formal models for real-world complex networks usually don't fully consider these geographical factors. We describe a flexible approach using a standard augmented graph model (e.g. Watt and Strogatz's [33], and Kleinberg's [20] models) and present important initial results on a refined model where we focus on the small-diameter characteristic and the above two geographical factors. We start with a general model where an arbitrary initial node-weighted graph H is augmented with additional random links specified by a generic 'distribution rule' τ and the weights of nodes in H. We consider a refined setting where the initial graph H is associated with a growth-bounded metric, and τ has a distance-bias characteristic, specified by parameters as follows. The base graph H has neighborhood growth bounded from both below and above, specified by parameters β1, β2 > 0. (These parameters can be thought of as the dimension of the graph, e.g. β1 = 2 and β1 = 3 for a graph modeling a setting with nodes in both 2D and 3D settings.) That is 2β1 ≤ Nu(2r)/Nu(r) ≤ 2β2 where Nu(r) is the number of nodes v within metric distance r from u: d(u, v) ≤ r. When we add random links using distribution τ, this distribution is specified by parameter α > 0 such that the probability that a link from u goes to v ≠ u is ∝ 1/dα(u,v). We show which parameters produce a small-diameter graph and how the diameter changes depending on the relationship between the distance-bias parameter α and the two bounded growth parameters β1, β2 > 0. In particular, for most connected base graphs, the diameter of our augmented graph is logarithmic if α ≤ β1, and poly-log if β2 ≤ α 2β2. These results also suggest promising implications for applications in designing routing algorithms.
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