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Sampling of temporal networks: Methods and biases

Sampling of temporal networks: Methods and biases

Rocha, Luis E. C. ORCID: 0000-0001-9046-8739, Masuda, Naoki and Holme, Petter (2017) Sampling of temporal networks: Methods and biases. Physical Review E, 96 (5). ISSN 2470-0045 (Print), 2470-0053 (Online) (doi:https://doi.org/10.1103/PhysRevE.96.052302)

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Abstract

Temporal networks have been increasingly used to model a diversity of systems that evolve in time; for example, human contact structures over which dynamic processes such as epidemics take place. A fundamental aspect of real-life networks is that they are sampled within temporal and spatial frames. Furthermore, one might wish to subsample networks to reduce their size for better visualization or to perform computationally intensive simulations. The sampling method may affect the network structure and thus caution is necessary to generalize results based on samples. In this paper, we study four sampling strategies applied to a variety of real-life temporal networks. We quantify the biases generated by each sampling strategy on a number of relevant statistics such as link activity, temporal paths and epidemic spread. We find that some biases are common in a variety of networks and statistics, but one strategy, uniform sampling of nodes, shows improved performance in most scenarios. Given the particularities of temporal network data and the variety of network structures, we recommend that the choice of sampling methods be problem oriented to minimize the potential biases for the specific research questions on hand. Our results help researchers to better design network data collection protocols and to understand the limitations of sampled temporal network data.

Item Type: Article
Additional Information: Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license.
Uncontrolled Keywords: Sampling, temporal networks
Faculty / Department / Research Group: Faculty of Business
Faculty of Business > Centre for Business Network Analysis
Faculty of Business > Department of International Business & Economics
Last Modified: 04 May 2018 15:00
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/19627

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