MODELING SOCIAL NETWORKS FROM SAMPLED DATA.

By Mark S. Handcock, Krista Gile. Annals of Applied Statistics, 2010

335 citations recorded by the source corpus.

Network models are widely used to represent relational information among interacting units and the structural implications of these relations. Recently, social network studies have focused a great deal of attention on random graph models of networks whose nodes represent individual social actors and whose edges represent a specified relationship between the actors. Most inference for social network models assumes that the presence or absence of all possible links is observed, that the information is completely reliable, and that there are no measurement (e.g., recording) errors. This is clearly not true in practice, as much network data is collected though sample surveys. In addition even if a census of a population is attempted, individuals and links between individuals are missed (i.e., do not appear in the recorded data). In this paper we develop the conceptual and computational theory for inference based on sampled network information. We first review forms of network sampling designs used in practice. We consider inference from the likelihood framework, and develop a typology of network data that reflects their treatment within this frame. We then develop inference for social network models based on information from adaptive network designs. We motivate and illustrate these ideas by analyzing the effect of link-tracing sampling designs on a collaboration network.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/7ba164e1eb4e20fe61955cbbf04871a787817c19

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Citations in the corpus

Exponential random graph models for multilevel networks (2013, background): “We cannot take a simple random sample of nodes in order to estimate a network model for ties within units, but Handcock and Gile (2010) and Pattison et al. (2011) have proposed conditional estimation strategies for snowball sampled network data where ERGM parameters can be obtained based on a…”
Potential networks, contagious communities, and understanding social network structure (2013, methodology): “for a survey of such results. In fact, graphs have traditionally been hard to sample and this is part of the reason that the newly acquired large-scale data are so welcome. Work by Handcock and Giles [15] proposes a method of estimating properties from adaptively sampled networks by using maximum li”