Rocha, Luis E. C. ORCID: https://orcid.org/0000-0001-9046-8739, Masuda, Naoki and Holme, Petter (2017) Sampling of temporal networks: methods and biases. Physical Review E, 96 (5):052302. ISSN 2470-0045 (Print), 2470-0053 (Online) (doi:10.1103/PhysRevE.96.052302)

Preview

PDF (Publisher's PDF - Open Access) 19627 ROCHA_Sampling_of_Temporal_Networks_(OA)_2017.pdf - Published Version Available under License Creative Commons Attribution. Download (810kB) | Preview

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
Subjects:	H Social Sciences > H Social Sciences (General)
Faculty / School / Research Centre / Research Group:	Faculty of Business Faculty of Business > Department of International Business & Economics Faculty of Business > Networks and Urban Systems Centre (NUSC) Faculty of Business > Networks and Urban Systems Centre (NUSC) > Centre for Business Network Analysis (CBNA)
Last Modified:	17 Mar 2020 15:24
URI:	http://gala.gre.ac.uk/id/eprint/19627

Actions (login required)

View Item

Downloads