# PageRank beyond the Web

@article{Gleich2015PageRankBT, title={PageRank beyond the Web}, author={David F. Gleich}, journal={ArXiv}, year={2015}, volume={abs/1407.5107} }

Google's PageRank method was developed to evaluate the importance of web-pages via their link structure. The mathematics of PageRank, however, are entirely general and apply to any graph or network in any domain. Thus, PageRank is now regularly used in bibliometrics, social and information network analysis, and for link prediction and recommendation. It's even used for systems analysis of road networks, as well as biology, chemistry, neuroscience, and physics. We'll see the mathematics and…

## 441 Citations

### Importance of intrinsic and non-network contribution in PageRank centrality and its effect on PageRank localization

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It is shown that PageRank value of a vertex also depends on its intrinsic, non-network contribution, and that localization of PageRank centrality depends upon the same intrinsic,non- network contribution.

### A Study of PageRank in Undirected Graphs

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This paper studies the PageRank sequence for undirected graphs of order six by PR vector, and provides an ordering for graphs by variance of PR vector which it’s variation is proportional with variance of degree sequence.

### Ranking Users in Social Networks With Higher-Order Structures

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This paper proposes a novel framework, motif-based PageRank (MPR), to incorporate higher-order structures into conventional PageRank computation, and conducts extensive experiments in three real-world networks to show that MPR can significantly improve the effectiveness of PageRank for ranking users in social networks.

### Strong Localization in Personalized PageRank Vectors

- Mathematics, Computer ScienceWAW
- 2015

An upper-bound on the number of entries necessary to approximate a personalized PageRank vector in graphs with skewed degree sequences is derived and shows localization under mild assumptions on the maximum and minimum degrees.

### Edinburgh Research Explorer Non-backtracking PageRank

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A variation of PageRank that uses a non-backtracking random walk is considered, deriving an explicit representation of the new algorithm that can exploit structure and sparsity in the underlying network.

### Boosting PageRank Scores by Optimizing Internal Link Structure

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A heuristic-based algorithm that achieves 100 times improvements of the minimum PageRank score among selected 100 vertices by adding only dozens of edges is proposed.

### Fatigued PageRank

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- 2021

This work formalize and exemplify the computation of Fatigued PageRank, evaluating it as a node ranking metric, as well as query-independent evidence in ad hoc document retrieval.

### Neighborhood and PageRank methods for pairwise link prediction

- Computer ScienceSocial Network Analysis and Mining
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A new link prediction task called “pairwise link prediction” is proposed that directly targets the prediction of new triangles, where one is tasked with finding which nodes are most likely to form a triangle with a given edge.

### PageRank Computation via Web Aggregation in Distributed Randomized Algorithms

- Computer Science, Mathematics2019 IEEE 58th Conference on Decision and Control (CDC)
- 2019

This paper presents extensions of the distributed algorithms which were recently proposed for the computation of PageRank that are modified for aggregation-based computation by grouping pages in the same domain.

### Efficient PageRank Computation via Distributed Algorithms with Web Clustering

- Computer Science, MathematicsArXiv
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This paper proposes a clustering-based scheme, in which groups of pages make updates by locally interacting among themselves many times to expedite the convergence of PageRank, which has significant advantages in their convergence performance.

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