Efficient web matrix processing based on dual reordering
Proceedings of the 17th ACM conference on Information and knowledge management
On computing PageRank via lumping the Google matrix
Journal of Computational and Applied Mathematics
Iterative aggregation: disaggregation methods and ordering algorithms
Proceedings of the 3rd International Conference on Performance Evaluation Methodologies and Tools
PageRank: Splitting Homogeneous Singular Linear Systems of Index One
ICTIR '09 Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory
PARA'06 Proceedings of the 8th international conference on Applied parallel computing: state of the art in scientific computing
Discover hierarchical subgraphs with network-topology based ranking score
Proceedings of the Third C* Conference on Computer Science and Software Engineering
An Arnoldi-Extrapolation algorithm for computing PageRank
Journal of Computational and Applied Mathematics
Convergence of multi-level iterative aggregation-disaggregation methods
Journal of Computational and Applied Mathematics
Retrieving keyworded subgraphs with graph ranking score
Expert Systems with Applications: An International Journal
Searching Steiner trees for web graph query
Computers and Industrial Engineering
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We describe a reordering particularly suited to the PageRank problem, which reduces the computation of the PageRank vector to that of solving a much smaller system and then using forward substitution to get the full solution vector. We compare the theoretical rates of convergence of the original PageRank algorithm to that of the new reordered PageRank algorithm, showing that the new algorithm can do no worse than the original algorithm. We present results of an experimental comparison on five datasets, which demonstrate that the reordered PageRank algorithm can provide a speedup of as much as a factor of 6. We also note potential additional benefits that result from the proposed reordering.