  Several network growth models have been proposed in the literature that
attempt to incorporate properties of citation networks. Generally, these models
aim at retaining the degree distribution observed in real-world networks. In
this work, we explore whether existing network growth models can realize the
diversity in citation growth exhibited by individual papers - a new
node-centric property observed recently in citation networks across multiple
domains of research. We theoretically and empirically show that the network
growth models which are solely based on degree and/or intrinsic fitness cannot
realize certain temporal growth behaviors that are observed in real-world
citation networks. To this end, we propose two new growth models that localize
the influence of papers through an appropriate attachment mechanism.
Experimental results on the real-world citation networks of Computer Science
and Physics domains show that our proposed models can better explain the
temporal behavior of citation networks than existing models.
