The generative model of feedback networks, studied by White, Kejžar, Tsallis, Farmer, or social-circles network model, defines a class of random graphs generated by simple processes that are common to edge formation and feedback loops in social circles. This class is distinct from the small-world network and the scale-free network models in network analysis but also captures many of the characteristics of real-world social networks.

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dbpprop:abstract
  • The generative model of feedback networks, studied by White, Kejžar, Tsallis, Farmer, or social-circles network model, defines a class of random graphs generated by simple processes that are common to edge formation and feedback loops in social circles. This class is distinct from the small-world network and the scale-free network models in network analysis but also captures many of the characteristics of real-world social networks. The implications of this type of model are carried further in Thurner, Kyriakopoulos and Tsallis, 2007, Unified Model for Network Dynamics Exhibiting Nonextensive Statistics Phys. Rev. E 76, 036111 (2007) (8 pages).
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  • The generative model of feedback networks, studied by White, Kejžar, Tsallis, Farmer, or social-circles network model, defines a class of random graphs generated by simple processes that are common to edge formation and feedback loops in social circles. This class is distinct from the small-world network and the scale-free network models in network analysis but also captures many of the characteristics of real-world social networks.
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  • Social-circles network model
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