  The internet structure is extremely complex. The Positive-Feedback Preference
(PFP) model is a recently introduced internet topology generator. The model
uses two generic algorithms to replicate the evolution dynamics observed on the
internet historic data. The phenomenological model was originally designed to
match only two topology properties of the internet, i.e. the rich-club
connectivity and the exact form of degree distribution. Whereas numerical
evaluation has shown that the PFP model accurately reproduces a large set of
other nontrivial characteristics as well. This paper aims to investigate why
and how this generative model captures so many diverse properties of the
internet. Based on comprehensive simulation results, the paper presents a
detailed analysis on the exact origin of each of the topology properties
produced by the model. This work reveals how network evolution mechanisms
control the obtained topology properties and it also provides insights on
correlations between various structural characteristics of complex networks.
