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Introduction
Modeling and understanding social network structure
has interested researchers from many backgrounds including
social science, computer science, theoretical physics and graph
theory. Notable models include achieving graphs
with power-law degree distribution using preferential attachment
and small-world characteristics using randomized rewiring of
a regular ring lattice respectively. In contrast to a body of
follow-up research which refine upon these seminal works to
better capture the graph structure and characteristics (such as
improving clustering coefficient by considering social triads along
with preferential attachment, this work aims additionally to
model the geographic spread in social networks. With increased
mobility in our society as well as enhanced communication
opportunities social networks are increasingly spread all over
the globe. Synthetic graphs imitating real-world social network
characteristics are often used for driving simulations for p
