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I am trying to sort out the implications of sampling from a known
set of ties in link-tracing designs with an unknown total node-set
and would appreciate help or related references.
For example, a web-crawler might be designed to sample 10% of the
links on a given webpage. The seed nodes are known and the nodes
that the crawler could sample from at each step are known (and
if/when those nodes also appear in subsequent sampled pages' links),
but the total population of nodes is not known.
Is it possible to draw legitimate conclusions on network properties
from such a data set, perhaps by using the sampling probabilities at
each stage of the design in a weighting system? Or does the nature
of the non-random selection on the dependent variable preclude such
Work I have found on link-tracing designs in ERGM (Pattison et al
2012, Handcock and Gile 2010, Koskinen et al 2013) assumes that
sampling takes place on nodes but not ties, e.g. that link-tracing
designs exhaustively sample the ties from given node-sets.
Respondent Driven Sampling uses link tracing without the assumption
of exhaustive sampling, but with non-sampled ties unknown (and other
restrictions that limit the ability to draw conclusions about the
The end goals of this question are twofold:
(1) To collect multi-wave online link-tracing data for network
analysis with less strain on (the collector's) computer resources
and (the pages of interest's) server resources
(2) I'm also hoping that if it were simpler to collect valid network
data with greater depth (number of waves) without producing as
intimidatingly large data sets, it might encourage more social
scientists without extensive computer science or mathematical
backgrounds to pursue internet-originated structural data that is
relevant to their research questions
Thanks for any help you have to offer.
Department of Sociology, Penn
Research Associate, The Association of
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