STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Stable algorithms for link analysis
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Using PageRank to Characterize Web Structure
COCOON '02 Proceedings of the 8th Annual International Conference on Computing and Combinatorics
FOCS '01 Proceedings of the 42nd IEEE symposium on Foundations of Computer Science
Link analysis, eigenvectors and stability
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Perturbation of the hyper-linked environment
COCOON'03 Proceedings of the 9th annual international conference on Computing and combinatorics
Models and algorithms for pagerank sensitivity
Models and algorithms for pagerank sensitivity
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We conduct an analysis of the sensitivity of three linear algebra-based ranking methods: the Colley, Massey, and Markov methods. Our analysis employs reverse engineering, in that we start with a simple input ranking vector that we use to build a perfect season, and we then determine the output rating vectors produced by the three methods. This analysis shows that the PageRank rating vector is strongly nonuniformly spaced, while the Colley and Massey methods provide a uniformly spaced rating vector, which is more natural for a perfect season. We further extend our study of the sensitivity and rank stability of these three methods with a careful perturbation analysis of the same perfect season dataset. We find that the Markov method is highly sensitive to small changes in the data and show with an example from the NFL that the Markov method's ranking vector displays some odd unstable behavior.