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UID:pretalx-2026-C7VA9Y@conferences.acspri.org.au
DTSTART;TZID=AEST:20261126T111500
DTEND;TZID=AEST:20261126T113000
DESCRIPTION:When collecting covert network data\, researchers prioritise fi
 nding edges without confirming the absence of a tie. Despite the theoretic
 al relevance of null ties\, it is clear that the identification of ties is
  more practical for the discovery of covert networks. Therefore\, we explo
 re the process by which covert networks are discovered as an edge sampling
  process. We develop the literature on edge sampling processes using the l
 ine graph and auto-logistic actor attribute model to systematically formul
 ate biased sampling processes to reflect realistic sampling biases. We def
 ine what might be termed person of interest (POI) bias in the model to ref
 lect the dependence between sampled edges. We present a few examples using
  empirical and simulated population networks. We conclude that biased samp
 ling processes can result in highly structured observed networks even when
  the population network lacks structure. We then briefly consider some pot
 ential applications of the model-based edge sampling process to adaptive s
 ampling design methodology.
DTSTAMP:20261013T065916Z
LOCATION:MacCallum Room
SUMMARY:Modelling covert network discovery as biased edge sampling - Assump
 tions and Extensions - Jonathan Januar
URL:https://conferences.acspri.org.au/2026/talk/C7VA9Y/
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