Cited by 295Quantum Computing Algorithms and ArchitectureComplex Network Analysis TechniquesGraph Theory and Algorithms

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Random Walks: A Review of Algorithms and Applications

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Xia, Feng, Liu, Jiaying, Nie, Hansong, Fu, Yonghao, Wan, Liangtian, Kong, Xiangjie·2020·IEEE Transactions on Emerging Topics in Computational Intelligence

DOI 10.1109/tetci.2019.2952908

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Abstract

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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Authors: Xia, F., Liu, J., Nie, H., Fu, Y., Wan, L., & Kong, X.
Affiliations: Federation University (AU)
Dalian University of Technology (CN)
ORCID: Xia, Feng
Liu, Jiaying
Nie, Hansong
Fu, Yonghao
Wan, Liangtian
Kong, Xiangjie
Venue: IEEE Transactions on Emerging Topics in Computational Intelligence
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2020-04
Volume: 4
Issue: 2
Pages: 95-107
ISSN: 2471-285X (ISSN-L)
DOI: 10.1109/tetci.2019.2952908
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: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
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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Random Walks: A Review of Algorithms and Applications

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