Geometric inhomogeneous random graphs
نویسندگان
چکیده
منابع مشابه
Geometric Inhomogeneous Random Graphs
For the theoretical study of real-world networks, we propose a model of scale-free randomgraphs with underlying geometry that we call geometric inhomogeneous random graphs (GIRGs).GIRGs generalize hyperbolic random graphs, which are a popular model to test algorithms forsocial and technological networks. Our generalization overcomes some limitations of hyperbolicrandom graphs, w...
متن کاملBootstrap Percolation on Geometric Inhomogeneous Random Graphs
Geometric inhomogeneous random graphs (GIRGs) are a model for scale-free networks with underlying geometry. We study bootstrap percolation on these graphs, which is a process modelling the spread of an infection of vertices starting within a (small) local region. We show that the process exhibits a phase transition in terms of the initial infection rate in this region. We determine the speed of...
متن کاملSampling Geometric Inhomogeneous Random Graphs in Linear Time
Real-world networks, like social networks or the internet infrastructure, have structural properties such as large clustering coefficients that can best be described in terms of an underlying geometry. This is why the focus of the literature on theoretical models for real-world networks shifted from classic models without geometry, such as Chung-Lu random graphs, to modern geometry-based models...
متن کاملInhomogeneous Random Graphs
The ‘classical’ random graphs, introduced by Erdős and Rényi half a century ago, are homogeneous in the sense that all their vertices play the same role and the degrees of the vertices tend to be concentrated around a typical value. Many graphs arising in the real world do not have this property, having, for example, power-law degree distributions. Thus there has been much recent interest in de...
متن کاملSusceptibility in Inhomogeneous Random Graphs
We study the susceptibility, i.e., the mean size of the component containing a random vertex, in a general model of inhomogeneous random graphs. This is one of the fundamental quantities associated to (percolation) phase transitions; in practice one of its main uses is that it often gives a way of determining the critical point by solving certain linear equations. Here we relate the susceptibil...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Theoretical Computer Science
سال: 2019
ISSN: 0304-3975
DOI: 10.1016/j.tcs.2018.08.014