Weighted PageRank algorithm
By W. Xing, Ali Akbar Ghorbani. Proceedings. Second Annual Conference on Communication Networks and Services Research, 2004., 2004
652 citations recorded by the source corpus.
Subfields: Information Systems, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/322293bb0bbd47349c5fd605dce5c63f03efb6a8
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Information Systems
- Junjun Yin
- Bin Jiang
- P. H. Hu
- Alejandro Jaimes
- S. Zhao
- David F. Gleich
- Tsang-Hsiang Cheng
- Gerald Matz
In Statistical and Nonlinear Physics
- Junjun Yin
- Bin Jiang
- P. H. Hu
- Alejandro Jaimes
- S. Zhao
- David F. Gleich
- Tsang-Hsiang Cheng
- Gerald Matz
Citations in the corpus
- Incorporating association rule networks in feature category-weighted naive Bayes model to support weaning decision making (2017, methodology): “Unlike the PageRank algorithm that tends to assign an equal rank value to each of the feature categories transitioning away, without considering the respective edge weights, ARFCWNB adopts a weighted PageRank algorithm [53] that assigns larger rank values to more important feature categories.”
- PageRank beyond the Web (2014, background): “Weighted PageRank [Xing and Ghorbani, 2004; Jiang, 2009] alters this assumption such that the walk preferentially visits high-degree nodes.”
- Graph Learning Based on Total Variation Minimization (2018, methodology): “GSP has been used to deal with applications ranging from online social networks [4–6], recommender systems [7], and proteomics [8, 9] (see [1] and [3] for further application examples).”
- A chronotherapeutics-applicable multi-target therapeutics based on AI: Example of therapeutic hypothermia (2022, background): “The weight of PageRank allows all nodes to be initially assigned with different weights/probabilities [37].”
- Image ranking based on user browsing behavior (2012, background): “Equation 2 looks similar to the weighted PageRank algorithm [32] but we are able to exploit additional information that is not available to web crawlers.”
- Characterizing the human mobility pattern in a large street network. (2008, methodology): “We adopt two types of random walkers, inspired by the PageRank algorithm (Page and Brin 1998) and its modified version, the weighted PageRank algorithm (Xing and Ghorbani 2004), hence they are referred to as PR-walkers and WPR-walkers.”