A Gradient Estimate for PageRank
| Autor: | Lauren M. Nelsen, Paul Horn |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Computer Science::Information Retrieval InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL Computer Science::Social and Information Networks Random walk Computer Science::Digital Libraries Teleportation Ranking (information retrieval) law.invention PageRank law Web page Applied mathematics Algorithm design Heat equation Constant (mathematics) MathematicsofComputing_DISCRETEMATHEMATICS |
| Zdroj: | Complex Networks and Their Applications VIII ISBN: 9783030366865 COMPLEX NETWORKS (1) |
| DOI: | 10.1007/978-3-030-36687-2_2 |
| Popis: | Personalized PageRank has found many uses in not only the ranking of webpages, but also algorithmic design, due to its ability to capture certain geometric properties of networks. In this paper, we study the diffusion of PageRank: how varying the jumping (or teleportation) constant affects PageRank values. To this end, we prove a gradient estimate for PageRank, akin to the Li-Yau inequality for positive solutions to the heat equation (for manifolds, with later versions adapted to graphs). |
| Databáze: | OpenAIRE |
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