Impact of local information in growing networks

Emanuele Massaro
(Università degli studi di Firenze)
Henrik Olsson
(Max Planck Institute for Human Development)
Andrea Guazzini
(Università degli studi di Firenze)
Franco Bagnoli
(Università degli studi di Firenze)

We present a new model of the evolutionary dynamics and the growth of on-line social networks. The model emulates people's strategies for acquiring information in social networks, emphasising the local subjective view of an individual and what kind of information the individual can acquire when arriving in a new social context. The model proceeds through two phases: (a) a discovery phase, in which the individual becomes aware of the surrounding world and (b) an elaboration phase, in which the individual elaborates locally the information trough a cognitive-inspired algorithm. Model generated networks reproduce main features of both theoretical and real-world networks, such as high clustering coefficient, low characteristic path length, strong division in communities, and variability of degree distributions.

In Alex Graudenzi, Giulio Caravagna, Giancarlo Mauri and Marco Antoniotti: Proceedings Wivace 2013 - Italian Workshop on Artificial Life and Evolutionary Computation (Wivace 2013), Milan, Italy, July 1-2, 2013, Electronic Proceedings in Theoretical Computer Science 130, pp. 53–60.
Published: 30th September 2013.

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