The DOI matches its Crossref record; 85 of the 87 references it cites were found in a registry or an index.
Random Walks: A Review of Algorithms and Applications
Open accessAbstract
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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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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Xia, F., Sun, K., Yu, S., Aziz, A., Wan, L., Pan, S., & Liu, H. (2021) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2021.3076021 · Cited by 534
Self-citationEdge Computing for Internet of Everything: A Survey
Kong, X., Wu, Y., Wang, H., & Xia, F. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3200431 · Cited by 323
Self-citationGraph Neural Networks for Intrusion Detection: A Survey
Bilot, T., Madhoun, N. E., Agha, K. A., & Zouaoui, A. (2023) · IEEE Access · doi:10.1109/access.2023.3275789 · Cited by 294
Fake Reviews Detection: A Survey
Mohawesh, R., Xu, S., Tran, S. N., Ollington, R., Springer, M., Jararweh, Y., & Maqsood, S. (2021) · IEEE Access · doi:10.1109/access.2021.3075573 · Cited by 238
Deepfake video detection: challenges and opportunities
Kaur, A., Noori Hoshyar, A., Saikrishna, V., Firmin, S., & Xia, F. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10810-6 · Cited by 214
“Deepfake leverages the power of artificial intelligence (AI) (Liu et al. 2018; Xia et al. 2019) to manipulate or generate visual and audio content with a high potential to deceive (Kietzmann et al. 2020).”
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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
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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.
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