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Open AccessArticle

Advanced Network Sampling with Heterogeneous Multiple Chains

1
College of Computer Science, Kookmin University, Seoul 02707, Korea
2
Department of Information and Telecommunication Engineering, Incheon National University, Incheon 22012, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Giorgio Terracina
Sensors 2021, 21(5), 1905; https://doi.org/10.3390/s21051905
Received: 26 January 2021 / Revised: 22 February 2021 / Accepted: 3 March 2021 / Published: 9 March 2021
(This article belongs to the Section Sensor Networks)
Recently, researchers have paid attention to many types of huge networks such as the Internet of Things, sensor networks, social networks, and traffic networks because of their untapped potential for theoretical and practical outcomes. A major obstacle in studying large-scale networks is that their size tends to increase exponentially. In addition, access to large network databases is limited for security or physical connection reasons. In this paper, we propose a novel sampling method that works effectively for large-scale networks. The proposed approach makes multiple heterogeneous Markov chains by adjusting random-walk traits on the given network to explore the target space efficiently. This approach provides better unbiased sampling results with reduced asymptotic variance within reasonable execution time than previous random-walk-based sampling approaches. We perform various experiments on large networks databases obtained from synthesis to real–world applications. The results demonstrate that the proposed method outperforms existing network sampling methods. View Full-Text
Keywords: internet of things; sensor networks; social network services; Network (Graph) Theory; big data; large-scale network; Network (Graph) Sampling Methods; data privacy internet of things; sensor networks; social network services; Network (Graph) Theory; big data; large-scale network; Network (Graph) Sampling Methods; data privacy
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MDPI and ACS Style

Lee, J.; Yoon, M.; Noh, S. Advanced Network Sampling with Heterogeneous Multiple Chains. Sensors 2021, 21, 1905. https://doi.org/10.3390/s21051905

AMA Style

Lee J, Yoon M, Noh S. Advanced Network Sampling with Heterogeneous Multiple Chains. Sensors. 2021; 21(5):1905. https://doi.org/10.3390/s21051905

Chicago/Turabian Style

Lee, Jaekoo; Yoon, MyungKeun; Noh, Song. 2021. "Advanced Network Sampling with Heterogeneous Multiple Chains" Sensors 21, no. 5: 1905. https://doi.org/10.3390/s21051905

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