Artículo
Memory effects induce structure in social networks with activity-driven agents
Fecha de publicación:
09/2014
Editorial:
Iop Publishing
Revista:
Journal Of Statistical Mechanics: Theory And Experiment
ISSN:
1742-5468
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
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 faceto-face encounters in a closed gathering, while the other one corresponding to social friendship ties from an online social network.
Palabras clave:
Stochastic Processes
,
Growth Processes
,
Network Dynamics
Archivos asociados
Licencia
Identificadores
Colecciones
Articulos(IFIBA)
Articulos de INST.DE FISICA DE BUENOS AIRES
Articulos de INST.DE FISICA DE BUENOS AIRES
Citación
Medus, A. D.; Dorso, Claudio Oscar; Memory effects induce structure in social networks with activity-driven agents; Iop Publishing; Journal Of Statistical Mechanics: Theory And Experiment; 2014; 9-2014; 1-23
Compartir
Altmétricas