Murase, YohsukeJo, Hang HyunTörök, JánosKertész, JánosKaski, Kimmo2025-04-042025-04-042019-05-152470-004510.1103/PhysRevE.99.052304https://hdl.handle.net/20.500.14018/27023Publisher Copyright: © 2019 authors. Published by the American Physical Society. Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.In a social network individuals or nodes connect to other nodes by choosing one of the channels of communication at a time to re-establish the existing social links. Since available data sets are usually restricted to a limited number of channels or layers, these autonomous decision making processes by the nodes constitute the sampling of a multiplex network leading to just one (though very important) example of sampling bias caused by the behavior of the nodes. We develop a general setting to get insight and understand the class of network sampling models, where the probability of sampling a link in the original network depends on the attributes h of its adjacent nodes. Assuming that the nodal attributes are independently drawn from an arbitrary distribution ρ(h) and that the sampling probability r(hi,hj) for a link ij of nodal attributes hi and hj is also arbitrary, we derive exact analytic expressions of the sampled network for such network characteristics as the degree distribution, degree correlation, and clustering spectrum. The properties of the sampled network turn out to be sums of quantities for the original network topology weighted by the factors stemming from the sampling. Based on our analysis, we find that the sampled network may have sampling-induced network properties that are absent in the original network, which implies the potential risk of a naive generalization of the results of the sample to the entire original network. We also consider the case, when neighboring nodes have correlated attributes to show how to generalize our formalism for such sampling bias and we get good agreement between the analytic results and the numerical simulations.enginfo:eu-repo/semantics/openAccessStatistical and Nonlinear PhysicsStatistics and ProbabilityCondensed Matter PhysicsJournal articlehttp://www.scopus.com/inward/record.url?scp=85065821880&partnerID=8YFLogxKMurase, Y, Jo, H H, Török, J, Kertész, J & Kaski, K 2019, 'Sampling networks by nodal attributes', Physical Review E - Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics, vol. 99, no. 5, 052304. https://doi.org/10.1103/PhysRevE.99.05230410646110