Introducing exponential random graph models for visibility networks

Abstract

Archaeological network analysts often represent archaeological data as static networks and explore their structure. However, most networks changed through time and static network representations do not allow archaeologists to test assumptions about the dynamic processes driving this change. The study of visibility networks in archaeology is a good example of this. Archaeologists propose hypotheses of the role of lines of sight between settlements, which imply dynamic processes for the establishment of the observed visibility networks. However, commonly used methods do not allow us to evaluate these hypotheses. In this paper we introduce exponential random graph modelling (ERGM) as a method for bridging static and dynamic approaches to interpreting visibility networks. This method offers a number of advantages: (1) it explicitly addresses the assumptions inherent in visibility network creation about what relationships between nodes mean and the types of processes they allow for; (2) it allows one to investigate the range of network structures that these assumptions give rise to; and (3) it explores the dynamic processes that might have led to observed networks. This method is used to evaluate hypotheses of the role of lines of sight in facilitating visual control and communication during the later Iron Age in Southern Spain. This study shows that ERGMs can be used as a reflective technique to evaluate competing hypotheses, and that ERGM results subsequently require more contextualised evaluation. Future work on ERGMs should focus on incorporating geographical constraints to further enhance its potential for studying visibility networks.