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Random Walks: A Review of Algorithms and Applications
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A random walk is known as a random process which describes a path including a succession of random steps in the mathematical space. It has increasingly been popular in various disciplines such as mathematics and computer science. Furthermore, in quantum mechanics, quantum walks can be regarded as quantum analogues of classical random walks. Classical random walks and quantum walks can be used to calculate the proximity between nodes and extract the topology in the network. Various random walk related models can be applied in different fields, which is of great significance to downstream tasks such as link prediction, recommendation, computer vision, semi-supervised learning, and network embedding. In this article, we aim to provide a comprehensive review of classical random walks and quantum walks. We first review the knowledge of classical random walks and quantum walks, including basic concepts and some typical algorithms. We also compare the algorithms based on quantum walks and classical random walks from the perspective of time complexity. Then we introduce their applications in the field of computer science. Finally we discuss the open issues from the perspectives of efficiency, main-memory volume, and computing time of existing algorithms. This study aims to contribute to this growing area of research by exploring random walks and quantum walks together.
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“Deepfake leverages the power of artificial intelligence (AI) (Liu etal. 2018; Xia etal. 2019) to manipulate or generate visual and audio content with a high potential to deceive (Kietzmann etal. 2020).”
From Deepfake video detection: challenges and opportunities (2024) · cites this paper as Xia et al. 2019
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Cites
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568
(1999) · Cited by 12,854
node2vec: Scalable Feature Learning for Networks
Grover, A., & Leskovec, J. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939754 · Cited by 10,926
DeepWalk: online learning of social representations
Perozzi, B., Al-Rfou, R., & Skiena, S. (2014) · Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2623330.2623732 · Cited by 10,123
A fast quantum mechanical algorithm for database search
Grover, L. K. (1996) · Proceedings of the twenty-eighth annual ACM symposium on Theory of computing - STOC '96 · doi:10.1145/237814.237866 · Cited by 8,830
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
Karypis, G., & Kumar, V. (1998) · SIAM Journal on Scientific Computing · doi:10.1137/s1064827595287997 · Cited by 5,984
GroupLens: an open architecture for collaborative filtering of netnews
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J. (1994) · Proceedings of the 1994 ACM conference on Computer supported cooperative work - CSCW '94 · doi:10.1145/192844.192905 · Cited by 5,058
Semi-Supervised Learning Literature Survey
Zhu, X. (2005) · Cited by 3,828
Semi-supervised learning using Gaussian fields and harmonic functions
Zhu, X., Zoubin Ghahramani, A., & Lafferty, J. (2003) · Cited by 3,509
The link‐prediction problem for social networks
Liben‐Nowell, D., & Kleinberg, J. M. (2007) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.20591 · Cited by 3,063
Research works printed but not linked: 2
- wang, 2019, Phys Rev Lett, 122
- 10.1007/978-1-4419-8462-3_3 · DOI
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Alam, M., Groth, P., de Boer, V., Pellegrini, T., Pandit, H. J., Montiel, E., Doncel, V. R., McGillivray, B. C., & Meroño-Peñuela, A. (2021) · Studies on the semantic web · doi:10.3233/ssw53 · Cited by 7
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Vontzos, G., Laitsos, V. M., Bargiotas, D., Fevgas, A., Daskalopulu, A., & Tsoukalas, L. H. (2025) · Electricity · doi:10.3390/electricity6020017 · Cited by 6
Dynamic network link prediction based on random walking and time aggregation
Zhang, M., Xu, B.-N., & Wang, L. (2023) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-023-01803-y · Cited by 6
Pérez-Gomariz, M., Cerdán, F., & García, J. (2025) · Computer Networks · doi:10.1016/j.comnet.2025.111181 · Cited by 8
Hu, J., Bewong, M., Kwashie, S., Zhang, Y., Nofong, V. M., Wondoh, J., & Feng, Z. (2025) · Information Systems · doi:10.1016/j.is.2025.102551 · Cited by 6
Muniyappan, S., Rayan, A. X. A., & Varrieth, G. T. (2023) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2023419 · Cited by 6
Fernandes, J. A., Azevedo, J. S., & Oliveira, S. P. (2024) · Discover Applied Sciences · doi:10.1007/s42452-024-06214-4 · Cited by 6
Dynamic state estimation of power system with stochastic delay based on neural network
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Metadata
- Authors
- Xia, F., Liu, J., Nie, H., Fu, Y., Wan, L., & Kong, X.
- Affiliations
- Federation University (AU)
- Dalian University of Technology (CN)
- Venue
- IEEE Transactions on Emerging Topics in Computational Intelligence
- Published
- 2020-04
- Volume
- 4
- Issue
- 2
- Pages
- 95-107
- ISSN
- 2471-285X (ISSN-L)
- OpenAlex
- W2990908872
- Cited by
- 295
- References
- 87
- Citations by year
- 2026: 48 · 2025: 64 · 2024: 52 · 2023: 54 · 2022: 44 · 2021: 25 · 2020: 8
- FWCI
- 8.88
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Emerging Topics in Computational Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- FedUni ResearchOnline (Federation University Australia) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61872054)
- Fundamental Research Funds for the Central Universities (DUT19LAB23, DUT18JC09)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Graph Theory and Algorithms
- Keywords
- random walks, quantum walks, classical random walks, network topology inference, node proximity, link prediction, graph embedding, semi-supervised learning, recommendations, time complexity
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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