Convenors:

Philip Verhagen, Vrije Universiteit Amsterdam, Department of Humanities

Tom Brughmans, University of Barcelona

Aline Deicke, Digital Academy, Academy of Sciences and Literature | Mainz

Natasa Djurdjevac Conrad, Zuse Institute Berlin

Grégoire van Havre, Universidade Federal do Piauí

Philip Riris, University College London

Explicitly including spatial or temporal information in network research is something that has come naturally to archaeologists. Our discipline has a long tradition of spatial analysis and of exploring long-term change in datasets and past phenomena. These are two areas where archaeologists did not look towards mathematicians, physicists and sociologists for inspiration, but rather developed original network methods based on a purely archaeological tradition. As such, they are some of the most promising research topics through which archaeologists can make unique contributions to network science.

But recognition of these contributions has still to materialise due to a number of challenges. How can we ensure these archaeology-inspired approaches become known, explored and applied in other disciplines? How precisely do these spatial and temporal archaeological approaches differ from existing network methods? What existing spatial and temporal approaches in archaeology show equal potential for inspiring new network research?

The spatial phenomena archaeologists address in their network research are rather narrow and can be grouped into three broad categories: movement-, visibility-, and interaction-related phenomena. The aim of network techniques in space syntax focus on exploring movement through urban space, whereas least-cost path networks tend to be used on landscape scales. Neither of these approaches have equivalents in network science (Verhagen et al. 2019). Archaeology has a strong tradition in visibility studies and is also pioneering its more diverse use in network research (Brughmans and Brandes 2017). Most visibility network analyses tend to explore theorised visual signalling networks or visual control over cultural and natural features. Most network methods used for exploring interaction potential between past communities or other cultural features belong to either absolute or relative distance approaches: such as maximum distance network, K-nearest neighbours (sometimes referred to as proximal point analysis (PPA)), beta-skeletons, relative neighbourhood network or Gabriel graph. These, however, are derived from computational geometry and have a long tradition in network research and computer science. Moreover, this is a not a field in which archaeologists seem to push the boundaries of network science (with perhaps a few exceptions; Knappett et al. 2008).

There are a few commonalities between the archaeological applications of these movement, visibility and interaction networks. They tend to be network data representations of traditional archaeological research approaches (e.g. viewsheds, least-cost paths, urban settlement structure, community interaction), and they tend to be applied on spatially large scales with the exception of space syntax (inter-island connectivity, landscape archaeology, regional visual signalling systems). How can we diversify spatial archaeological network research? How can we go beyond making network copies of what archaeologists have done before and rather draw on the unique feature of network data (the ability to formally represent dependencies) to develop even more original spatial network techniques? This seems to us like an eminently possible task for archaeologists.

Despite being at the core of archaeological research, the use of temporal (or longitudinal) network data is common but incredibly narrow in archaeological network research. By far the most common application is to consider dating evidence for nodes or edges and to chop up the resulting networks into predefined categories that could have a typological, culture historical or chronological logic (e.g. artefact type A; Roman Republican; 400-300 BC). This process results in subnetworks sometimes referred to as snapshots, the structure of which are explored in chronological order like a filmstrip. A significantly less common approach is to represent processes of network structural change as dynamic network models (e.g. Bentley et al. 2005), or to represent dynamic processes taking place on top of network structures (e.g. Graham 2006).

This research focus of temporal archaeological network research is not at all representative of the diverse and critical ways archaeologists study temporal change. How can the archaeological research tradition inspire new temporal network approaches? How can the use of dynamic network models become more commonly applied? What temporal approaches from network science have archaeologists neglected to adopt? How can, for example, studies modelling the evolution of networks suggest explanations for the levels of complexity observed in past networks?

This session welcomes papers on archaeological network research including but not exclusive to these challenges. We also invite you to present your work on the topics of missing data, cross-disciplinary collaboration and teaching networks in the linked session ‘Archaeological network research 2’.

References

Bentley, R., Lake, M., & Shennan, S. (2005). Specialisation and wealth inequality in a model of a clustered economic network. Journal of Archaeological Science, 32(9), 1346–1356. https://doi.org/10.1016/j.jas.2005.03.008

Brughmans, T., & Brandes, U. (2017). Visibility network patterns and methods for studying visual relational phenomena in archaeology. Frontiers in Digital Humanities: Digital Archaeology, 4(17). https://doi.org/doi.org/10.3389/fdigh.2017.00017

Graham, S. (2006). Networks, Agent-Based Models and the Antonine Itineraries: Implications for Roman Archaeology. Journal of Mediterranean Archaeology, 19(1), 45–64. https://doi.org/10.1558/jmea.2006.19.1.45

Knappett, C., Evans, T., & Rivers, R. (2008). Modelling maritime interaction in the Aegean Bronze Age. Antiquity, 82(318), 1009–1024. Retrieved from http://antiquity.ac.uk/Ant/082/1009/ant0821009.pdf

Verhagen, P., Nuninger, L. & Groenhuijzen, M. R. (2019). Modelling of pathways and movement networks in archaeology: an overview of current approaches. In: Verhagen, P., J. Joyce & M.R. Groenhuijzen (eds.) Finding the Limits of the Limes: Modelling Demography, Economy and Transport on the Edge of the Roman Empire. Cham: Springer, p. 217-249. https://doi.org/10.1007/978-3-030-04576-0_11

Convenors:

Grégoire van Havre, Universidade Federal do Piauí – Department of Archaeology

Tom Brughmans, University of Barcelona

Aline Deicke, Digital Academy, Academy of Sciences and Literature | Mainz

Natasa Djurdjevac Conrad, Zuse Institute Berlin

Grégoire van Havre, Universidade Federal do Piauí

Philip Riris, University College London

Philip Verhagen, Vrije Universiteit Amsterdam, Department of Humanities

New challenges emerge as network research becomes ever more common in archaeology: can we develop new network methods for dealing with missing archaeological data, how can cross-disciplinary collaborations be leveraged to make original contributions to both archaeology and network science, and how do we teach archaeological network research in the classroom?

Although a range of techniques exist in both archaeology and network science for dealing with missing data and data uncertainty, the fragmentation of the material record presents a challenge – made more explicit through the use of formal methods – that is hard to tackle. Much of the task of identifying network science equivalents of archaeological missing data techniques remains to be done, and there is a real need for identifying how archaeological approaches could lead to the development of new network mathematical and statistical techniques. But by far most pressing is the need to formally express data uncertainty and absence in our archaeological network research.

Like many other aspects of archaeological network research, this challenge should be faced through cross-disciplinary collaboration with mathematicians, statisticians and physicists. Archaeological network research has a great track record of such collaborations, but not all of them have been successful and not all archaeologists find it equally easy to identify collaborators in other disciplines. How can we facilitate the communication between scholars with different disciplinary backgrounds? How can we foster archaeological network research that holds potential contributions to archaeology as well as other disciplines? What events and resources should be developed to provide a platform for cross-disciplinary contact and collaboration?

Now that archaeological network research is slowly becoming recognised as an archaeological subdiscipline in its own right, the topic increasingly finds itself in the curriculum of postgraduate modules and summer schools. But this rapid growth is almost exclusively marked by research and has neglected the development of teaching resources and approaches. What resources are necessary? What lines of argumentation and case studies are particularly powerful for convincing students of the need to see network research as part of our discipline? Which foundations (e.g. data literacy, statistics, and more) have to be laid to facilitate the widespread adoption of formal methods in general into our research processes?

This session welcomes papers on archaeological network research including but not exclusive to these new challenges.  We also invite you to present your work on the topics of spatial and temporal networks in the linked session ‘Archaeological network research 1’.