%0 Book Section
%A Amblard, Frederic
%A Quattrociocchi, Walter
%B Simulating social complexity: a handbook
%D 2013
%F eprints:2114
%I Springer 
%K Topics: Computer Appl. in Social and Behavioral Sciences;  Social Sciences, general; Statistical Physics, Dynamical Systems and Complexity; Behavioural Sciences; Game Theory, Economics, Social and Behav. Sciences; Computing methodologies    
%P 401-430
%S Understanding Complex Systems
%T Social networks and spatial distribution
%U http://eprints.imtlucca.it/2114/
%X In most agent-based social simulation models, the issue of the organisation of the agents’ population matters. The topology, in which agents interact, – be it spatially structured or a social network – can have important impacts on the obtained results in social simulation. Unfortunately, the necessary data about the target system is often lacking, therefore you have to use models in order to reproduce realistic spatial distributions of the population and/or realistic social networks among the agents. In this chapter we identify the main issues concerning this point and describe several models of social networks or of spatial distribution that can be integrated in agent-based simulation to go a step forward from the use of a purely random model. In each case we identify several output measures that allow quantifying their impacts.