  Under preferential attachment (PA) network growth models late arrivals are at
a disadvantage with regard to their final degrees. Previous extensions of PA
have addressed this deficiency by either adding the notion of node fitness to
PA, usually drawn from some fitness score distributions, or by using fitness
alone to control attachment. Here we introduce a new dynamical approach to
address late arrivals by adding a recent-degree-change bias to PA so that nodes
with higher relative degree change in temporal proximity to an arriving node
get an attachment probability boost. In other words, if PA describes a
rich-get-richer mechanism, and fitness-based approaches describe
good-get-richer mechanisms, then our model can be characterized as a
hot-get-richer mechanism, where hotness is determined by the rate of degree
change over some recent past. The proposed model produces much later
high-ranking nodes than the PA model and, under certain parameters, produces
networks with structure similar to PA networks.
