The dynamics of power laws: Fitness and aging in preferential attachment trees
arXiv:1703.05943 · doi:10.1007/s10955-017-1841-8
Abstract
Continuous-time branching processes describe the evolution of a population whose individuals generate a random number of children according to a birth process. Such branching processes can be used to understand preferential attachment models in which the birth rates are linear functions. We are motivated by citation networks, where power-law citation counts are observed as well as aging in the citation patterns. To model this, we introduce fitness and age-dependence in these birth processes. The multiplicative fitness moderates the rate at which children are born, while the aging is integrable, so that individuals receives a finite number of children in their lifetime. We show the existence of a limiting degree distribution for such processes. In the preferential attachment case, where fitness and aging are absent, this limiting degree distribution is known to have power-law tails. We show that the limiting degree distribution has exponential tails for bounded fitnesses in the presence of integrable aging, while the power-law tail is restored when integrable aging is combined with fitness with unbounded support with at most exponential tails. In the absence of integrable aging, such processes are explosive.
41 pages, 10 figures
References in corpus (7)
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- Learning the mechanisms of network growth
- A version of Herbert A. Simon's model with slowly fading memory and its connections to branching processes
- A short-ranged memory model with preferential growth