A spatial model for social networks
arXiv:physics/0505128 · doi:10.1016/j.physa.2005.04.029
Abstract
We study spatial embeddings of random graphs in which nodes are randomly distributed in geographical space. We let the edge probability between any two nodes to be dependent on the spatial distance between them and demonstrate that this model captures many generic properties of social networks, including the ``small-world'' properties, skewed degree distribution, and most distinctively the existence of community structures.
To be published in Physica A (2005)
References in corpus (10)
- Statistical mechanics of complex networks
- The structure and function of complex networks
- Fast algorithm for detecting community structure in networks
- The structure of scientific collaboration networks
- Random graphs with arbitrary degree distributions and their applications
- Mixing patterns in networks
- Classes of behavior of small-world networks
- The statistical mechanics of networks
- Network Transitivity and Matrix Models
- How to search a social network
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