Dense Power-law Networks and Simplicial Complexes

TL;DR

This paper introduces a new network growth model based on the Pitman-Yor process that can generate dense, scale-free networks and simplicial complexes, addressing limitations of previous models that only produced sparse networks.

Contribution

The authors develop a novel framework for modeling dense, scale-free networks and simplicial complexes using the Pitman-Yor process, extending beyond traditional preferential attachment models.

Findings

The model produces undirected scale-free networks with exponent γ=2.

It generates directed networks with tunable power-law out-degree exponents in (1,2).

The framework extends to dense directed simplicial complexes with power-law generalized out-degree distributions.

Abstract

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 $P(k)\propto k^{-\gamma}$ with $\gamma\in(1,2]$. 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 scale-free networks with exponent $\gamma=2$ or directed networks with power-law out-degree distribution with tunable exponent $\gamma \in (1,2)$. 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 data sets 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.