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 accessAbstract
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)
- 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.
