  Complex networks have non-trivial characteristics and appear in many
real-world systems. Such networks are vitally important, but their full
underlying dynamics are not completely understood. Utilizing new data sources,
however, can unveil the evolution process of these networks.
  This study uses the recently published Reddit dataset, containing over 1.65
billion comments, to construct the largest publicly available social network
corpus to date. We used this dataset to deeply examine the network evolution
process, which resulted in two key observations: First, links are more likely
to be created among users who join a network at a similar time. Second, the
rate in which new users join a network is a central factor in molding a
network's topology; i.e., different user-join patterns create different
topological properties.
  Based on these observations, we developed the \textit{Temporal Preferential
Attachment} random network generation model. This model produces not only
scale-free networks that have relative high clustering coefficients, but also
networks that are sensitive to both the rate and the time in which users join
the network. This results in a more accurate and flexible model of how complex
networks evolve, one which more closely represents real-world data.
