Computational and Mathematical Organization Theory

, Volume 23, Issue 3, pp 362–390 | Cite as

Generating and analyzing spatial social networks

  • Meysam AlizadehEmail author
  • Claudio Cioffi-Revilla
  • Andrew Crooks


In this paper, we propose a class of models for generating spatial versions of three classic networks: Erdös-Rényi (ER), Watts-Strogatz (WS), and Barabási-Albert (BA). We assume that nodes have geographical coordinates, are uniformly distributed over an m × m Cartesian space, and long-distance connections are penalized. Our computational results show higher clustering coefficient, assortativity, and transitivity in all three spatial networks, and imperfect power law degree distribution in the BA network. Furthermore, we analyze a special case with geographically clustered coordinates, resembling real human communities, in which points are clustered over k centers. Comparison between the uniformly and geographically clustered versions of the proposed spatial networks show an increase in values of the clustering coefficient, assortativity, and transitivity, and a lognormal degree distribution for spatially clustered ER, taller degree distribution and higher average path length for spatially clustered WS, and higher clustering coefficient and transitivity for the spatially clustered BA networks.


Spatial social networks Network properties Random network Small-world network Scale-free network 



This study was supported in part by the Center for Social Complexity and the Computational Social Science program within the Department of Computational and Data Sciences at George Mason University. M. Alizadeh is funded by a GMU Presidential Fellowship and, together with C. Cioffi-Revilla, by ONR-Minerva Grant No. N00014130054.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Meysam Alizadeh
    • 1
    • 2
    Email author
  • Claudio Cioffi-Revilla
    • 1
    • 2
  • Andrew Crooks
    • 1
    • 2
  1. 1.Computational Social Science Program, Department of Computational and Data SciencesGeorge Mason UniversityFairfaxUSA
  2. 2.Center for Social Complexity, Krasnow Institute for Advanced StudiesGeorge Mason UniversityFairfaxUSA

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