The University of Southampton
University of Southampton Institutional Repository

Likelihood-based assessment of dynamic networks

Likelihood-based assessment of dynamic networks
Likelihood-based assessment of dynamic networks
This paper deals with the problem of assessing probabilistic models that represent the evolution of a target graph. Such models have long been a topic of interest for a number of networks, especially communications networks. The solution developed in this paper gives a rigorous way to calculate the likelihood of the observed graph evolution having arisen from a wide variety of hypothesized models encompassing many already present in the literature. The framework is shown to recover parameters from artificial data and is tested on real data sets from Facebook and from emails from the company Enron.
2051-1310
1-18
Clegg, Richard
994a2792-68a5-4a96-8530-d4a09128b4ad
Parker, Ben
26c5a5ab-17b3-4d6c-ae11-abf3a2554529
Rio, Miguel
b6a22a93-a53c-4487-be23-55834358c539
Clegg, Richard
994a2792-68a5-4a96-8530-d4a09128b4ad
Parker, Ben
26c5a5ab-17b3-4d6c-ae11-abf3a2554529
Rio, Miguel
b6a22a93-a53c-4487-be23-55834358c539

Clegg, Richard, Parker, Ben and Rio, Miguel (2016) Likelihood-based assessment of dynamic networks. Journal of Complex Networks, 1-18. (doi:10.1093/comnet/cnv031).

Record type: Article

Abstract

This paper deals with the problem of assessing probabilistic models that represent the evolution of a target graph. Such models have long been a topic of interest for a number of networks, especially communications networks. The solution developed in this paper gives a rigorous way to calculate the likelihood of the observed graph evolution having arisen from a wide variety of hypothesized models encompassing many already present in the literature. The framework is shown to recover parameters from artificial data and is tested on real data sets from Facebook and from emails from the company Enron.

Text
feta_comnet_2015.pdf - Accepted Manuscript
Download (283kB)

More information

Accepted/In Press date: 14 December 2015
e-pub ahead of print date: 23 March 2016
Organisations: Statistical Sciences Research Institute

Identifiers

Local EPrints ID: 397485
URI: http://eprints.soton.ac.uk/id/eprint/397485
ISSN: 2051-1310
PURE UUID: 2a71d69f-5531-41f2-b892-781b39dbc8ce

Catalogue record

Date deposited: 01 Jul 2016 14:04
Last modified: 15 Mar 2024 05:42

Export record

Altmetrics

Contributors

Author: Richard Clegg
Author: Ben Parker
Author: Miguel Rio

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×