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The study of social networks --- where people are located, geographically,and how they might be connected to one another --- is a current hot topic ofinterest, because of its immediate relevance to important applications, fromdevising efficient immunization techniques for the arrest of epidemics, to thedesign of better transportation and city planning paradigms, to theunderstanding of how rumors and opinions spread and take shape over time. Wedevelop a spatial social complex network (SSCN) model that captures not onlyessential connectivity features of real-life social networks, including aheavy-tailed degree distribution and high clustering, but also the spatiallocation of individuals, reproducing Zipf's law for the distribution of citypopulations as well as other observed hallmarks. We then simulate Milgram'sSmall-World experiment on our SSCN model, obtaining good qualitative agreementwith the known results and shedding light on the role played by various networkattributes and the strategies used by the players in the game. Thisdemonstrates the potential of the SSCN model for the simulation and study ofthe many social processes mentioned above, where both connectivity andgeography play a role in the dynamics.

Modeling spatial social complex networks for dynamical processes