Abstract

Network analysis has been increasingly used in archaeology during the last two decades and is a promising approach to understanding past. However, doing network analysis adds another layer of difficulty because not only the data’s sparseness often presents a problem for several network metrics and models but also defining the network itself can be challenging.

Mesopotamian hollow ways are physical remains of past people’s movements and can tell us about the connectivity of the societies that produced them. They are structures that, in theory, lend themselves well for network analysis: physical edges that connect sites, i.e. nodes, with each other. However, these data are partial, with gaps in the routes, and the settlement record is – as far as we know – far from complete.

In this paper, we present computational methods to enhance fragmented archaeological data. Two algorithms were developed to overcome the issue of missing data for a) the network of Bronze-Age hollow ways in Mesopotamia and b) the settlement system for the same period and region. The improved data sets will be used as the input of exponential random graph models (ERGM) and agent-based-models (ABM) both of which will be described as well.