# PageRank beyond the Web

@article{Gleich2015PageRankBT, title={PageRank beyond the Web}, author={David F. Gleich}, journal={SIAM Rev.}, year={2015}, volume={57}, pages={321-363} }

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… Expand

#### 381 Citations

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

- Computer Science, Mathematics
- ArXiv
- 2016

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. Expand

A Study of PageRank in Undirected Graphs

- Mathematics
- 2019

The PageRank (PR) algorithm is the base of Google search engine. In this paper, we study the PageRank sequence for undirected graphs of order six by PR vector. Then, we provide an ordering for graphs… Expand

Ranking Users in Social Networks With Higher-Order Structures

- Computer Science
- AAAI
- 2018

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. Expand

Distributed Randomized Algorithms for PageRank Based on a Novel Interpretation

- Computer Science
- 2018 Annual American Control Conference (ACC)
- 2018

Gossip-type randomization is employed in the update schemes, and it is shown that the page selection need not be limited to the uniform distribution. Expand

Boosting PageRank Scores by Optimizing Internal Link Structure

- Computer Science
- DEXA
- 2018

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. Expand

Strong Localization in Personalized PageRank Vectors

- Computer Science, Mathematics
- WAW
- 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. Expand

Neighborhood and PageRank methods for pairwise link prediction

- Computer Science
- Social Network Analysis and Mining
- 2020

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. Expand

PageRank Computation via Web Aggregation in Distributed Randomized Algorithms

- Computer Science
- 2019 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. Expand

Block models and personalized PageRank

- Mathematics, Computer Science
- Proceedings of the National Academy of Sciences
- 2016

A principled framework for evaluating ranking methods by studying seed set expansion applied to the stochastic block model is developed and the optimal gradient is derived, surprisingly, that under reasonable assumptions the gradient is asymptotically equivalent to personalized PageRank for a specific choice of the PageRank parameter α that depends on the block model parameters. Expand

Efficient PageRank Computation via Distributed Algorithms with Web Clustering

- Computer Science, Engineering
- ArXiv
- 2019

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. Expand

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