Memory effects induce structure in social networks with activity-driven agents

Activity-driven modelling has recently been proposed as an alternative growth mechanism for time varying networks,displaying power-law degree distribution in time-aggregated representation. This approach assumes memoryless agents developing random connections with total disregard of their previous c...

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Detalles Bibliográficos
Autores principales:	Medus, A.D., Dorso, C.O.
Formato:	JOUR
Materias:	analysis of algorithms growth processes network dynamics stochastic processes (theory)
Acceso en línea:	http://hdl.handle.net/20.500.12110/paper_17425468_v2014_n9_p_Medus
Aporte de:	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

id	todo:paper_17425468_v2014_n9_p_Medus
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spelling	todo:paper_17425468_v2014_n9_p_Medus2023-10-03T16:30:23Z Memory effects induce structure in social networks with activity-driven agents Medus, A.D. Dorso, C.O. analysis of algorithms growth processes network dynamics stochastic processes (theory) Activity-driven modelling has recently been proposed as an alternative growth mechanism for time varying networks,displaying power-law degree distribution in time-aggregated representation. This approach assumes memoryless agents developing random connections with total disregard of their previous contacts. Thus, such an assumption leads to time-aggregated random networks that do not reproduce the positive degree-degree correlation and high clustering coefficient widely observed in real social networks. In this paper, we aim to study the incidence of the agents' long-term memory on the emergence of new social ties. To this end, we propose a dynamical network model assuming heterogeneous activity for agents, together with a triadic-closure step as main connectivity mechanism. We show that this simple mechanism provides some of the fundamental topological features expected for real social networks in their time-aggregated picture. We derive analytical results and perform extensive numerical simulations in regimes with and without population growth. Finally, we present an illustrative comparison with two case studies, one comprising face-to-face encounters in a closed gathering, while the other one corresponding to social friendship ties from an online social network. © 2014 IOP Publishing Ltd and SISSA Medialab srl. Fil:Dorso, C.O. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_17425468_v2014_n9_p_Medus
institution	Universidad de Buenos Aires
institution_str	I-28
repository_str	R-134
collection	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic	analysis of algorithms growth processes network dynamics stochastic processes (theory)
spellingShingle	analysis of algorithms growth processes network dynamics stochastic processes (theory) Medus, A.D. Dorso, C.O. Memory effects induce structure in social networks with activity-driven agents
topic_facet	analysis of algorithms growth processes network dynamics stochastic processes (theory)
description	Activity-driven modelling has recently been proposed as an alternative growth mechanism for time varying networks,displaying power-law degree distribution in time-aggregated representation. This approach assumes memoryless agents developing random connections with total disregard of their previous contacts. Thus, such an assumption leads to time-aggregated random networks that do not reproduce the positive degree-degree correlation and high clustering coefficient widely observed in real social networks. In this paper, we aim to study the incidence of the agents' long-term memory on the emergence of new social ties. To this end, we propose a dynamical network model assuming heterogeneous activity for agents, together with a triadic-closure step as main connectivity mechanism. We show that this simple mechanism provides some of the fundamental topological features expected for real social networks in their time-aggregated picture. We derive analytical results and perform extensive numerical simulations in regimes with and without population growth. Finally, we present an illustrative comparison with two case studies, one comprising face-to-face encounters in a closed gathering, while the other one corresponding to social friendship ties from an online social network. © 2014 IOP Publishing Ltd and SISSA Medialab srl.
format	JOUR
author	Medus, A.D. Dorso, C.O.
author_facet	Medus, A.D. Dorso, C.O.
author_sort	Medus, A.D.
title	Memory effects induce structure in social networks with activity-driven agents
title_short	Memory effects induce structure in social networks with activity-driven agents
title_full	Memory effects induce structure in social networks with activity-driven agents
title_fullStr	Memory effects induce structure in social networks with activity-driven agents
title_full_unstemmed	Memory effects induce structure in social networks with activity-driven agents
title_sort	memory effects induce structure in social networks with activity-driven agents
url	http://hdl.handle.net/20.500.12110/paper_17425468_v2014_n9_p_Medus
work_keys_str_mv	AT medusad memoryeffectsinducestructureinsocialnetworkswithactivitydrivenagents AT dorsoco memoryeffectsinducestructureinsocialnetworkswithactivitydrivenagents
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Memory effects induce structure in social networks with activity-driven agents

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