Dense Power-law Networks and Simplicial Complexes

نویسندگان

  • Owen T. Courtney
  • Ginestra Bianconi
چکیده

There is increasing evidence that dense networks occur in on-line social networks, recommendation networks and in the brain. In addition to being dense, these networks are often also scale-free, i.e. their degree distributions follow P (k) ∝ k−γ with γ ∈ (1, 2]. Models of growing networks have been successfully employed to produce scale-free networks using preferential attachment, however these models can only produce sparse networks as the numbers of links and nodes being added at each time-step is constant. Here we present a modelling framework which produces networks that are both dense and scale-free. The mechanism by which the networks grow in this model is based on the Pitman-Yor process. Variations on the model are able to produce undirected scalefree networks with exponent γ = 2 or directed networks with power-law out-degree distribution with tunable exponent γ ∈ (1, 2). We also extend the model to that of directed 2-dimensional simplicial complexes. Simplicial complexes are generalization of networks that can encode the many body interactions between the parts of a complex system and as such are becoming increasingly popular to characterize different datasets ranging from social interacting systems to the brain. Our model produces dense directed simplicial complexes with power-law distribution of the generalized out-degrees of the nodes.

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عنوان ژورنال:
  • CoRR

دوره abs/1802.01465  شماره 

صفحات  -

تاریخ انتشار 2018