In this chapter we take a step back from our in-depth methodological overview to describe what we see as some of the possible future trajectories of productive and critical network research in archaeology. Networks are already beginning to help us address a broad range of archaeological questions, as we have seen in this book (see discussion in Chapter 2), and networks are certainly useful tools and analytical constructs for many common archaeological tasks. We see the increased importance of network methods in archaeology as a trend that is likely to continue. From this vantage point, we ask: What are the profitable next steps that might push network thinking and archaeological network research to the next level? How can network methods and theories help us toward new answers for old archaeological questions or even toward questions we have not yet considered? Can archaeologists contribute to the world of network science beyond archaeology? We believe that network science has transformative potential for archaeological research and that archaeologists can be important players in network science in general, if and only if explicitly formulated relational theories drive network research in the field going forward.

Are my data good enough to create an archaeological network? What if I am missing some sites or contexts, or I have poor or variable quality information for some observations? Can I still apply network methods and models with incomplete and/or imperfect data, or should I not even attempt to use network methods? At this point, some of you may be asking yourselves questions along these lines. It is good to carefully consider potential data issues when conducting any archaeological analysis, but there are also some specific concerns revolving around sampling and data quality that deserve special attention when dealing with network data. In this chapter, we outline some of the most common issues you will encounter and further offer a generalized approach to identifying and assessing the potential impacts of sources of variation and uncertainty in network data through simulation and resampling.

Most of the past phenomena we study as archaeologists took place in physical space: individuals lived in homes and towns, and they moved through landscapes; they fought wars on battlefields and they exchanged goods from faraway places. Through our excavations, fieldwork, and literature studies we record spatial information such as the outlines of houses, the locations of sites, the slopes of terrain, or the distance between natural resources and settlements. Many relational phenomena are explicitly geographical, in that the medium of geographical space is an important aspect of the relationship itself. For example, road segments connect pairs of settlements that are close together, and lines of sight connect places from which observers can see features. Such phenomena could be quite straightforwardly represented as spatial networks since the nodes and edges are both explicitly embedded in physical space. But for other relational phenomena, space is more like a background feature that can be brought into analyses when relevant but does not feature prominently in the definition of either nodes or edges. For example, past food webs where species are connected through trophic flows or social networks where individuals are connected to their contacts both involve entities (nodes) and relationships (edges) that have spatial properties or attributes, but those spatial properties are not directly invoked in the definition of such networks. We refer to these as networks in space in that we could include spatial features into their network representations, but this is not explicitly included in their definition.

Networks are nothing more than a set of entities and the pairwise connections among them. This simple definition encompasses a tremendous amount of variation from communication systems like the internet to power grids to neurons in the brain to road systems and flights between airports to our own social networks defined through familial ties, acquaintance, or any manner of interaction one could imagine. Over the last 20 years or so, academic interest in networks and the complex properties of network systems has grown by leaps and bounds. This has been mirrored by a growing excitement by the public in general (see best-selling works including Barabási and Frangos 2014 and Watts 2004). It is not uncommon these days to see networks and network visuals used as explanatory tools in news stories or popular articles shared across social media (another kind of network) exploring the complicated connections among characters in television shows, books, or people and organizations involved in news stories. Everyone, it seems, is excited about networks and networks are everywhere.

The purpose of this chapter is to give you the basic lay of the land in the world of archaeological network research in order to provide context for the remainder of the book. As we saw in Chapter 1, although archaeologists have applied graph-theoretic and network analytic methods toward archaeological questions for more than 50 years, it is really only in the last 10 years or so that such approaches have become common. Archaeological network science is still quite a young subdiscipline and is constantly changing. There are likely to be some “growing pains” as we all figure out how to best adopt, adapt, and develop network methods appropriate for archaeological data and archaeological questions. This is perhaps not too different from where specializations like GIS were in archaeology 15–20 years ago (see Connolly and Lake 2006; Wheatley and Gillings 2002).