Activity driven modeling of time varying networks

Network modeling plays a critical role in identifying statistical regularities and structural principles common to many systems. The large majority of recent modeling approaches are connectivity driven. The structural patterns of the network are at the basis of the mechanisms ruling the network form...

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Main Authors:	Perra, N., Gonçalves, B., Pastor-Satorras, R., Vespignani, A.
Format:	Online
Language:	English
Published:	Nature Publishing Group 2012
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3384079/

id	pubmed-3384079
recordtype	oai_dc
spelling	pubmed-33840792012-06-27 Activity driven modeling of time varying networks Perra, N. Gonçalves, B. Pastor-Satorras, R. Vespignani, A. Article Network modeling plays a critical role in identifying statistical regularities and structural principles common to many systems. The large majority of recent modeling approaches are connectivity driven. The structural patterns of the network are at the basis of the mechanisms ruling the network formation. Connectivity driven models necessarily provide a time-aggregated representation that may fail to describe the instantaneous and fluctuating dynamics of many networks. We address this challenge by defining the activity potential, a time invariant function characterizing the agents' interactions and constructing an activity driven model capable of encoding the instantaneous time description of the network dynamics. The model provides an explanation of structural features such as the presence of hubs, which simply originate from the heterogeneous activity of agents. Within this framework, highly dynamical networks can be described analytically, allowing a quantitative discussion of the biases induced by the time-aggregated representations in the analysis of dynamical processes. Nature Publishing Group 2012-06-25 /pmc/articles/PMC3384079/ /pubmed/22741058 http://dx.doi.org/10.1038/srep00469 Text en Copyright © 2012, Macmillan Publishers Limited. All rights reserved http://creativecommons.org/licenses/by-nc-sa/3.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-ShareALike 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Perra, N. Gonçalves, B. Pastor-Satorras, R. Vespignani, A.
spellingShingle	Perra, N. Gonçalves, B. Pastor-Satorras, R. Vespignani, A. Activity driven modeling of time varying networks
author_facet	Perra, N. Gonçalves, B. Pastor-Satorras, R. Vespignani, A.
author_sort	Perra, N.
title	Activity driven modeling of time varying networks
title_short	Activity driven modeling of time varying networks
title_full	Activity driven modeling of time varying networks
title_fullStr	Activity driven modeling of time varying networks
title_full_unstemmed	Activity driven modeling of time varying networks
title_sort	activity driven modeling of time varying networks
description	Network modeling plays a critical role in identifying statistical regularities and structural principles common to many systems. The large majority of recent modeling approaches are connectivity driven. The structural patterns of the network are at the basis of the mechanisms ruling the network formation. Connectivity driven models necessarily provide a time-aggregated representation that may fail to describe the instantaneous and fluctuating dynamics of many networks. We address this challenge by defining the activity potential, a time invariant function characterizing the agents' interactions and constructing an activity driven model capable of encoding the instantaneous time description of the network dynamics. The model provides an explanation of structural features such as the presence of hubs, which simply originate from the heterogeneous activity of agents. Within this framework, highly dynamical networks can be described analytically, allowing a quantitative discussion of the biases induced by the time-aggregated representations in the analysis of dynamical processes.
publisher	Nature Publishing Group
publishDate	2012
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3384079/
_version_	1611539321965248512

Activity driven modeling of time varying networks

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