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

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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

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