PageRank beyond the Web

By David F. Gleich. SIAM Review, 2014

731 citations recorded by the source corpus.

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 ideas that unite these diverse applications.

Subfields: Artificial Intelligence, Information Systems, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/dffeb59e22a977b0b9a3c9ee1994dcffb030414d

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Citations in the corpus

The science of science: from the perspective of complex systems (2017, background): “For more complete generalizations and applications of PageRank, readers are encouraged to refer to two recent review articles [176,177].”
GOTCHA! Network-Based Fraud Detection for Social Security Fraud (2017, methodology): “We start from the Personalized PageRank algorithm (Page et al. 1998), one of the popular applications of the Random Walk with Restarts (RWR) method (Gleich 2014).”
Random Walks on Simplicial Complexes and the normalized Hodge Laplacian (2018, methodology): “This close relationship between Markov chains and graphs has led to a broad adoption of diffusion-based algorithms in network science [87], with applications to ranking connected objects [59], analyzing disease spreading [111], and respondentdriven sampling [112].”
Opinion Dynamics and the Evolution of Social Power in Influence Networks (2015, background): “We refer the reader to [34] for a recent extensive survey of eigenvector centrality, related notions, and applications.”
Vital nodes identification in complex networks (2016, methodology): “PageRank has been utilized far beyond its initial intent and design [101].”
Random walks and diffusion on networks (2016, background): “, PageRank [23, 66] and eigenvector centrality [67]) are derived 86”
What are higher-order networks? (2021, background): “determine important nodes [112,123,224], reveal modular structure of a system [108, 249], or—if each node is a dynamical unit—elucidate collective network dynamics such as synchronization [269].”