論文

2019年5月

Sampling networks by nodal attributes

PHYSICAL REVIEW E
  • Yohsuke Murase
  • ,
  • Hang-Hyun Jo
  • ,
  • Janos Torok
  • ,
  • Janos Kertesz
  • ,
  • Kimmo Kaski

99
5
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1103/PhysRevE.99.052304
出版者・発行元
AMER PHYSICAL SOC

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 rho(h) and that the sampling probability r(h(i), h(j)) for a link i j of nodal attributes h(i) and h(j) 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.


リンク情報
DOI
https://doi.org/10.1103/PhysRevE.99.052304
arXiv
http://arxiv.org/abs/arXiv:1902.04707
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000468224900003&DestApp=WOS_CPL
URL
http://arxiv.org/abs/1902.04707v2
URL
http://arxiv.org/pdf/1902.04707v2 本文へのリンクあり
ID情報
  • DOI : 10.1103/PhysRevE.99.052304
  • ISSN : 2470-0045
  • eISSN : 2470-0053
  • arXiv ID : arXiv:1902.04707
  • Web of Science ID : WOS:000468224900003

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