  This work extends the personalized PageRank model invented by Brin and Page
to a family of PageRank models with various damping schemes. The goal with
increased model variety is to capture or recognize a larger number of types of
network activities, phenomena and propagation patterns. The response in
PageRank distribution to variation in damping mechanism is then characterized
analytically, and further estimated quantitatively on 6 large real-world link
graphs. The study leads to new observation and empirical findings. It is found
that the difference in the pattern of PageRank vector responding to parameter
variation by each model among the 6 graphs is relatively smaller than the
difference among 3 particular models used in the study on each of the graphs.
This suggests the utility of model variety for differentiating network
activities and propagation patterns. The quantitative analysis of the damping
mechanisms over multiple damping models and parameters is facilitated by a
highly efficient algorithm, which calculates all PageRank vectors at once via a
commonly shared, spectrally invariant subspace. The spectral space is found to
be of low dimension for each of the real-world graphs.
