Abstract

In this paper we consider the clustering coefficient, and clustering function in a random graph model proposed by Krioukov et al. in 2010. In this model, nodes are chosen randomly inside a disk in the hyperbolic plane and two nodes are connected if they are at most at a certain hyperbolic distance from each other. It has been previously shown that this model has various properties associated with complex networks, including a power-law degree distribution, “short distances” and a non-vanishing clustering coefficient. The model is specified using three parameters: the number of nodes $n$, which we think of as going to infinity, and $\alpha , \nu > 0$, which we think of as constant. Roughly speaking, the parameter $\alpha $ controls the power law exponent of the degree sequence and $\nu $ the average degree. Here we show that the clustering coefficient tends in probability to a constant $\gamma $ that we give explicitly as a closed form expression in terms of $\alpha , \nu $ and certain special functions. This improves earlier work by Gugelmann et al., who proved that the clustering coefficient remains bounded away from zero with high probability, but left open the issue of convergence to a limiting constant. Similarly, we are able to show that $c(k)$, the average clustering coefficient over all vertices of degree exactly $k$, tends in probability to a limit $\gamma (k)$ which we give explicitly as a closed form expression in terms of $\alpha , \nu $ and certain special functions. We are able to extend this last result also to sequences $(k_{n})_{n}$ where $k_{n}$ grows as a function of $n$. Our results show that $\gamma (k)$ scales differently, as $k$ grows, for different ranges of $\alpha $. More precisely, there exists constants $c_{\alpha ,\nu }$ depending on $\alpha $ and $\nu $, such that as $k \to \infty $, $\gamma (k) \sim c_{\alpha ,\nu } \cdot k^{2 - 4\alpha }$ if $\frac {1}{2} < \alpha < \frac {3}{4}$, $\gamma (k) \sim c_{\alpha ,\nu } \cdot \log (k) \cdot k^{-1}$ if $\alpha =\frac {3}{4}$ and $\gamma (k) \sim c_{\alpha ,\nu } \cdot k^{-1}$ when $\alpha > \frac {3}{4}$. These results contradict a claim of Krioukov et al., which stated that $\gamma (k)$ should always scale with $k^{-1}$ as we let $k$ grow.

Highlights

  • In this paper we consider the clustering coefficient, and clustering function in a random graph model proposed by Krioukov et al in 2010

  • It exhibits the three main characteristics usually associated with complex networks: a power-law degree distribution, a non-vanishing clustering coefficient and short graph distances

  • In this work we study the clustering coefficient in the KPKVB model

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Summary

Introduction and main results

We will consider clustering in a model of random graphs that involves points taken randomly in the hyperbolic plane. This model was introduced by Krioukov, Papadopoulos, Kitsak, Vahdat and Boguñá [25] in 2010 – we abbreviate it as the KPKVB model. We should note that the model goes by several other names in the literature, including hyperbolic random geometric graphs and random hyperbolic graphs. Krioukov et al suggested this model as a suitable model for complex networks. It exhibits the three main characteristics usually associated with complex networks: a power-law degree distribution, a non-vanishing clustering coefficient and short graph distances

KPKVB model
2: Plot of γ for α varying from
Outline of the paper
Preliminaries
The finite box model Gbox
The Poissonized KPKVB model GPo
Coupling GPo and Gbox
The Campbell-Mecke formula
Concentration of heights
The degree of the typical point
The expected clustering coefficient of the typical point
Proof overview
Expected degrees in Gbox and GPo
Joint degrees in Gbox and GPo
Factorial moments of degrees
Coupling Gn to GPo
Adjusted clustering and the Poissonized KPKVB model
Coupling of local clustering between GPo and Gbox
Counting missing triangles
The main contribution of triangles
Joint degrees in Gbox
Concentration result for main triangle contribution
Equivalence for local clustering in GPo and Gbox
Some results on the hyperbolic geometric graph
Equivalence clustering GPo and Gbox
E Concentration of heights for vertices with degree k
Concentration of heights argument for the infinite model
Concentration of heights for the KPKVB and finite box model
MR-3708706
Full Text
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