Jump to ContentJump to Main Navigation

You are looking at 1-5 of 5 items

  • Keywords: configuration model x
Clear All Modify Search

View:

Uncorrelated networks

Sergey N. Dorogovtsev

in Lectures on Complex Networks

Published in print:
2010
Published Online:
May 2010
ISBN:
9780199548927
eISBN:
9780191720574
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780199548927.003.0005
Subject:
Physics, Theoretical, Computational, and Statistical Physics

This chapter considers networks with an arbitrary degree distribution, in which the degrees of nodes are uncorrelated, including the degrees of the nearest-neighbour nodes. The two kinds of these ... More


The configuration model

Mark Newman

in Networks

Published in print:
2018
Published Online:
October 2018
ISBN:
9780198805090
eISBN:
9780191843235
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/oso/9780198805090.003.0012
Subject:
Physics, Theoretical, Computational, and Statistical Physics

A discussion of the most fundamental of network models, the configuration model, which is a random graph model of a network with a specified degree sequence. Following a definition of the model a ... More


Percolation and network resilience

Mark Newman

in Networks

Published in print:
2018
Published Online:
October 2018
ISBN:
9780198805090
eISBN:
9780191843235
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/oso/9780198805090.003.0015
Subject:
Physics, Theoretical, Computational, and Statistical Physics

A discussion of the site percolation process on networks and its application as a model of network resilience. The chapter starts with a description of the percolation process, in which nodes are ... More


Network growth algorithms

A.C.C. Coolen, A. Annibale, and E.S. Roberts

in Generating Random Networks and Graphs

Published in print:
2017
Published Online:
May 2017
ISBN:
9780198709893
eISBN:
9780191780172
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/oso/9780198709893.003.0008
Subject:
Physics, Theoretical, Computational, and Statistical Physics

Growth processes are a fundamentally different approach compared to probability-driven exponential models covered in earlier chapters. This chapter studies how growth rules can be designed to mimic ... More


Modelling

Guido Caldarelli and Alessandro Chessa

in Data Science and Complex Networks: Real Case Studies with Python

Published in print:
2016
Published Online:
December 2016
ISBN:
9780199639601
eISBN:
9780191782916
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780199639601.003.0007
Subject:
Physics, Theoretical, Computational, and Statistical Physics

Once network structures are established it is important to be able to reproduce in a computer the system we are studying. We can use models to predict the outcome of an experiment, or to understand ... More


View: