Multidamping simulation framework for link-based ranking

We review methods for the approximate computation of PageRank. Standard methods are based on the eigenvector and linear system characterizations. Our starting point are recent methods based on series representation whose coefficients are damping functions, for example Linear Rank, HyperRank and TotalRank, etc. We propose a multidamping framework for interpreting PageRank and these methods. Multidamping is based on some new useful properties of Google type matrices. The approach can be generalized and could help in the exploration of new approximations for list-based ranking. This is joint work with Georgios Kollias and is supported by a Pythagoras-EPEAEK-II grant