Abstract
Model-based reconstruction is an approach to infer network structures where they cannot be observed. For archaeological networks, several models based on assumptions concerning distance among sites, site size, or costs and benefits have been proposed to infer missing ties. Since these assumptions are formulated at a dyadic level, they do not provide means to express dependencies among ties and therefore include less plausible network scenarios. In this paper we investigate the use of network models that explicitly incorporate tie dependence. In particular, we consider exponential random graph models, and show how they can be applied to reconstruct networks coherent with Burt's arguments on closure and structural holes (Burt 2001). The approach is illustrated on data from the Middle Bronze Age in the Aegean.
Highlights
Network approaches have been adopted in many disciplines to analyse relational data
We focus on Exponential Random Graph Models (ERGMs), a family of models used to describe the structure of an observed network
We propose to establish appropriate magnitudes by first estimating parameters of the specified exponential random graph models (ERGMs) on a network obtained from one of the models described in Section 2 and tuning these estimated parameters according to the underlying assumptions
Summary
Network approaches have been adopted in many disciplines to analyse relational data. Archaeology is no exception as attested by pioneering studies in the late 60s and 70s (Clarke 1972; Terrell 1977) and recent applications (Knappett 2011; Brughmans 2013). The networks resulting from MDN and PPA are binary (a tie from i to j is either present or absent) and determined by the geographical location of the sites or by the number of sustainable connections.
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