We show that heterogeneous degree distributions in observed scale-free topologies of complex networks can emerge as a consequence of the exponential expansion of hidden hyperbolic space. Fermi-Dirac statistics provides a physical interpretation of hyperbolic distances as energies of links. The hidden space curvature affects the heterogeneity of the degree distribution, while clustering is a function of temperature. We embed the internet into the hyperbolic plane and find a remarkable congruency between the embedding and our hyperbolic model. Besides proving our model realistic, this embedding may be used for routing with only local information, which holds significant promise for improving the performance of internet routing. © 2009 The Ame...
In this paper we consider the clustering coefficient, and clustering function in a random graph mode...
\u3cp\u3eIn this paper we study weighted distances in scale-free spatial network models: hyperbolic ...
We show that the community structure of a network can be used as a coarse version of its embedding i...
Two common features of many large real networks are that they are sparse and that they have strong c...
Through detailed analysis of scores of publicly available data sets corresponding to a wide range of...
The node degrees of large real-world networks often follow a power-law distribution. Such scale-free...
\u3cp\u3eRandom graphs with power-law degrees can model scale-free networks as sparse topologies wit...
We present a general class of geometric network growth mechanisms by homogeneous attachment in which...
The theme of this paper is the study of typical distances in a ran-dom graph model that was introduc...
Network science is driven by the question which properties large real-world networks have and how we...
We obtain the degree distribution for a class of growing network models on flat and curved spaces. T...
There is a complex relation between the mechanism of preferential attachment, scale-free degree dist...
Clustering is a fundamental property of complex networks and it is the mathematical expression of a ...
Defining the geometry of networks is typically associated with embedding in low-dimensional spaces s...
Recent years have shown a promising progress in understanding geometric underpinnings behind the st...
In this paper we consider the clustering coefficient, and clustering function in a random graph mode...
\u3cp\u3eIn this paper we study weighted distances in scale-free spatial network models: hyperbolic ...
We show that the community structure of a network can be used as a coarse version of its embedding i...
Two common features of many large real networks are that they are sparse and that they have strong c...
Through detailed analysis of scores of publicly available data sets corresponding to a wide range of...
The node degrees of large real-world networks often follow a power-law distribution. Such scale-free...
\u3cp\u3eRandom graphs with power-law degrees can model scale-free networks as sparse topologies wit...
We present a general class of geometric network growth mechanisms by homogeneous attachment in which...
The theme of this paper is the study of typical distances in a ran-dom graph model that was introduc...
Network science is driven by the question which properties large real-world networks have and how we...
We obtain the degree distribution for a class of growing network models on flat and curved spaces. T...
There is a complex relation between the mechanism of preferential attachment, scale-free degree dist...
Clustering is a fundamental property of complex networks and it is the mathematical expression of a ...
Defining the geometry of networks is typically associated with embedding in low-dimensional spaces s...
Recent years have shown a promising progress in understanding geometric underpinnings behind the st...
In this paper we consider the clustering coefficient, and clustering function in a random graph mode...
\u3cp\u3eIn this paper we study weighted distances in scale-free spatial network models: hyperbolic ...
We show that the community structure of a network can be used as a coarse version of its embedding i...