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Cited by 657

The DOI matches its Crossref record; 272 of the 272 references it cites were found in a registry or an index.

Random walks and diffusion on networks

Open access
Masuda, Naoki, Porter, Mason A., Lambiotte, Renaud·2017·Physics Reports
DOI 10.1016/j.physrep.2017.07.007
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Abstract

Random walks are ubiquitous in the sciences, and they are interesting from both theoretical and practical perspectives. They are one of the most fundamental types of stochastic processes; can be used to model numerous phenomena, including diffusion, interactions, and opinions among humans and animals; and can be used to extract information about important entities or dense groups of entities in a network. Random walks have been studied for many decades on both regular lattices and (especially in the last couple of decades) on networks with a variety of structures. In the present article, we survey the theory and applications of random walks on networks, restricting ourselves to simple cases of single and non-adaptive random walkers. We distinguish three main types of random walks: discrete-time random walks, node-centric continuous-time random walks, and edge-centric continuous-time random walks. We first briefly survey random walks on a line, and then we consider random walks on various types of networks. We extensively discuss applications of random walks, including ranking of nodes (e.g., PageRank), community detection, respondent-driven sampling, and opinion models such as voter models.

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Metadata

Authors
Masuda, N., Porter, M. A., & Lambiotte, R.
Venue
Physics Reports
Publisher
Elsevier BV
Published
2017-11
Volume
716-717
Pages
1-58
ISSN
0370-1573 (ISSN-L)
DOI
10.1016/j.physrep.2017.07.007
OpenAlex
W2566971417
Cited by
657
References
272
FWCI
27.52
Type
journal article
Language
en
Open access
hybrid

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.