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Using Non-Linear Dynamical Systems for Web Searching and Ranking

Summary: Introduces non-linear link-analysis ranking algorithms as an alternative to eigenvector methods (HITS/PAGERANK). For a key special case proves global convergence and characterizes combinatorial structure of stationary weights; shows strong empirical results. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1308
Venue
PODS
Year
2004
Pagerank
5.093636e-05
Overall Rank
12,762 | 12.45%
DOI
10.1145/1055558.1055569

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Authors

BibTeX Citation

@inproceedings{tsaparas_pods04,
        address = {New York, NY, USA},
        series = {{PODS} '04},
        title = {{Using Non-Linear Dynamical Systems for Web Searching and Ranking}},
        url = {https://dl.acm.org/doi/10.1145/1055558.1055569},
        doi = {10.1145/1055558.1055569},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Tsaparas, Panayiotis},
        year = {2004}
}

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
1,713 Clustering Categorical Data: An Approach Based on Dynamical Systems 1998 VLDB 9.94811e-05
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