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}
}
  • D. Gleich
  • Published 2015
  • Computer Science, Mathematics, Physics
  • SIAM Rev.
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
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381 Citations
Highly Influential Citations
31
Background Citations
152
Methods Citations
92
Results Citations
4

381 Citations

Citation Type

Citation Type

Importance of intrinsic and non-network contribution in PageRank centrality and its effect on PageRank localization
TLDR
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
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
TLDR
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
TLDR
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
TLDR
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
TLDR
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
TLDR
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
  • A. Suzuki, H. Ishii
  • Computer Science
  • 2019 IEEE 58th Conference on Decision and Control (CDC)
  • 2019
TLDR
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
TLDR
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
TLDR
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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References

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TLDR
Several modifications of the classical PageRank formula adapted for bibliographic networks take into account not only the citation but also the co-authorship graph and turn out to be “better” than the standard PageRank ranking. Expand
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TLDR
The research community has devoted an increased attention to reduce the computation time needed by Web ranking algorithms, and the core of PageRank exploits an iterative weight assignment of ranks to the Web pages, until a fixed point is reached. Expand
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TLDR
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TLDR
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TLDR
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TLDR
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TLDR
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TLDR
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TLDR
Two modifications to the adjacency matrix input to the HITS algorithm are presented, which weights links according to how often they were followed by users in a given time period and incorporates user feedback without requiring direct user querying. Expand
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