Caribbean views: two new publications

My colleagues and friends doubt my professionalism when I show Caribbean views like this, taken whilst “working”. The last two years I’ve actually been working quite hard, but it’s just difficult to make Caribbean archaeology look like hard work. It was an absolute privilege to meet and learn from colleagues and friends in a number of Caribbean islands, about their knowledge of the landscapes that surround them and their relationship to the peoples who lived there before them. The current natural disasters befalling many of these islands, and their fall-out, are terrible. I wish all my friends there much strength and hope their respective governments (and aid workers around the world) do their duties in taking care of their residents.

Some of my work in Caribbean archaeology has now been published, and is available for free! (Download links: archaeological paper; methodological paper)

A first paper, co-authored with Maaike de Waal, Corinne Hofman and Ulrik Brandes, explores what we can learn about Amerindian social networks by examining Caribbean views: how places are connected based on what can be seen from them. It applies a wide range of computational methods (visibility networks, total viewsheds, visual neighbourhood configurations), but it should not be seen as a methodological exercise. The paper aimed to express some of the ideas that Caribbean archaeologists have formulated about how views could have mattered to past peoples, because they could be used for navigation, to share information through smoke or fire signalling, or to determine suitable settlement locations. Doing so led to some unique insights into the connectivity of landscapes in Eastern Guadeloupe (the paper’s research area), that led us to formulate a theory about the structuring role played by views in the pre-colonial Lesser Antilles as a whole: short-distance views at which people or smoke signals could be seen structured placement of settlements and community interactions locally, within regions on landmasses; whereas long-distance views at which only huge landmasses could be seen would structure navigation between communities on different landmasses. We see the Lesser Antilles as consisting of thousands of local connectivity  clusters, all connected through the long-distance visibility of landmasses (see figure below).

brughmans-etal-guadeloupe-fig10
The Eastern Guadeloupe study area, showing important long distance views connecting settlements (dotted lines) and clusters of short distance views between settlements (solid lines).

A second paper, co-authored with Ulrik Brandes, vastly expands the methodological toolbox for visibility network methods. Having reviewed the archaeological use of formal methods for studying visibility phenomena (i.e. what people in the past could see), we noticed that there was a discrepancy between the theories formulated and the methods used to explore them. The theories were often very complex, involving many different ways in which visibility could have structured past human behaviour and could have affected past human decision-making. Few of these theories have been explored using formal methods, often because of their share complexity, and those that have been treated formally were explored with a very limited range of formal methods: mainly binary viewsheds and simple visibility network representation. So we thought there was some fun methodological work to be done here, that could benefit future archaeological (and other) research. We approached visibility as a purely relational phenomenon, connecting the observer’s eyes with the observed feature. Doing so allowed us to represent any kind of visibility study to be represented as networks, which led to some really cool new network representation. For example (see figure below), a cumulative view shed can be represented as a two-mode network where observation points like site locations are connected to the landscape locations that can be observed from them. This two-network can be split up into two one-mode networks: a network where sites are connected if they have landscape locations in common that can be seen from both, and a network where landscape locations are connected if they have sites in common from which both can be observed. In addition, we also explored how complex theories of visibility can be teased apart into their constituent parts, where each part is represented by a small network data representation. We can count the frequency of these patterns and even simulate the preferential creation of these patterns, to explore how probable our complex theories are.

I will write more about these studies at a later time. All of this work was funded by EU HERA and ERC Synergy funding. Don’t hesitate to get in touch if you like this kind of thing!

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The Connected Past special issue of Journal of Archaeological Method and Theory out now!

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I am super massively chuffed to announce that The Connected Past special issue of the Journal of Archaeological Method and theory is out now. It aims to provide examples of the critical and innovative use of network science in archaeology in order to inspire its more widespread use. What’s even better, the editorial is open access! And it’s accompanied by a glossary of network science techniques and concepts that we hope will prove to be a useful resource for archaeologists interested in network concepts.

My fellow editors Anna Collar, Fiona Coward, Barbara Mills and I are extremely grateful to all the authors of this special issue for their great contributions. You can read in the editorial the details of why we think these contributions are great. We would also like to thank the editors of Journal of Archaeological Method and Theory for offering us great support throughout the process, and to Springer for agreeing to make the editorial open access.

Original papers in this issue (Gotta read ’em all!):

Networks in Archaeology: Phenomena, Abstraction, Representation
by the editors Anna Collar, Fiona Coward, Tom Brughmans, and Barbara J. Mills

Are Social Networks Survival Networks? An Example from the Late Pre-Hispanic US Southwest
by Lewis Borck, Barbara J. Mills, Matthew A. Peeples, and Jeffery J. Clark

Understanding Inter-settlement Visibility in Iron Age and Roman Southern Spain with Exponential Random Graph Models for Visibility Networks
by Tom Brughmans, Simon Keay, and Graeme Earl

Inferring Ancestral Pueblo Social Networks from Simulation in the Central Mesa Verde
by Stefani A. Crabtree

Network Analysis of Archaeological Data from Hunter-Gatherers: Methodological Problems and Potential Solutions
by Erik Gjesfjeld

Procurement and Distribution of Pre-Hispanic Mesoamerican Obsidian 900 BC–AD 1520: a Social Network Analysis
by Mark Golitko, and Gary M. Feinman

The Equifinality of Archaeological Networks: an Agent-Based Exploratory Lab Approach
by Shawn Graham, and Scott Weingart

Remotely Local: Ego-networks of Late Pre-colonial (AD 1000–1450) Saba, North-eastern Caribbean
by Angus A. A. Mol, Menno L. P. Hoogland, and Corinne L. Hofman

The Diffusion of Fired Bricks in Hellenistic Europe: A Similarity Network Analysis
by Per Östborn, and Henrik Gerding

I got a top cited article! What does that mean?!?

Yesterday the Research Excellence Framework results were published, and it was therefore a nice coincidence to be notified by Springer yesterday that my paper is one of the top cited papers in Journal of Archaeological Method and Theory of 2013/2014. You can see it on this picture:

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I am really happy and grateful about this. However, it did make me wonder what it means in numbers to have a top cited article. The answer is rather sobering: not much! In this blog post I will have a little look around citation land, and share some take-home messages about citation and impact in archaeology with you. Read on until the end, and you might find a call for revolution in the academic publishing world! 🙂

The source mentioned is ISI/Thomson Reuters database, and luckily I can access their metrics through Web of Science. A quick search revealed this paper has 8 citations on Web of Science (all databases), see the figure below:

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That’s a sobering eyeopener! Especially considering one of these 8 citations is by a paper I wrote myself. This tells me quite a lot about the impact of the Journal of Archaeological Method and Theory, about the bit of archaeology that I am specialised in, and about that part of archaeologists’ citation behaviour represented by Web of Science.

Let’s start by that last one. Web of Science only indexes publications (mainly journals) with a long and consistent editorial board and publication history, focusing almost exclusively on English as the language of science. It defends this policy by stating the fact that the majority of all citations (about 60% or so) cite papers in a minority of journals (I believe about 20%, but don’t cite me on this). So there’s a clear tendency here to include high impact publications. Archaeology does not have many journals of high impact with a long tradition and a stable editorial history, whilst English is definitely NOT the only language of academic archaeology which is mainly due to the need to publish excavation reports in the local language. From my citation network analysis work I get the impression that less than half of all citations are included in Web of Science.

Why do I know that? Well let’s compare my 8 citation in Web of Science with how many this paper got according to Google Scholar:

jamt3So according to Google Scholar this paper was cited 16 times. Now Google Scholar does not care so much about the language or format of publication, so a much larger number of publications is indexed. But these citations also include those that are usually not included in any impact scores, such as citations mentioned on presentation slides or poster uploaded to the internet.

Take-home message number 1: check the citations to your paper on multiple citation databases before bragging about your impact (Web of Science, Google Scholar, Scopus).

What about the Journal of Archaeological Method and Theory? It is not the highest rated journal in archaeology, but I do think it’s up there in the top ten or so. But the top ten of what? Journals are usually ranked by their impact factor, which is the measure introduced by the Institute for Scientific Information using the data you can access through Web of Science. It represents the average number of citations in the last few years per paper in a journal. Here some Impact Factor results of the Journal of Archaeological Method and Theory:

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In 2013 ISI gave it an impact factor of 1.389 which ranked it 18th in Anthropology, just below Antiquity and just above American Antiquity. These rankings are published yearly by ISI as the Journal Citation Reports. But there are more measures than just the Impact Factor. Google Scholar uses the h5 index to rank journals in disciplines: “the h5 index is the h-index for articles published in the last 5 complete years. It is the largest number h such that h articles published in 2009-2013 have at least h citations each”. In the category of Archaeology the Journal of Archaeological Method and Theory has an h5 index of 13 and ranks 15th: lower than American Antiquity and dwarfed by the scores of Journal of Archaeological Science (38) and Antiquity (21).

These measures of impact give you an idea of the number of citations on average a paper in a journal receives. This is not solely a result of a paper’s own merit or infamy. It should at least in part be seen as an effect of the journal itself being widely read, so papers published in well-known journals attract more citations because they adopt the visibility of the journal they are published in.

But citation practices differ greatly between disciplines. A quantitative measure of impact might therefore not be particularly relevant for all disciplines. For the humanities a more qualitative interpretation of impact is available: the European Reference Index for the Humanities. The site was down when I wrote this blog post, but the idea is simple. It gives a journal one of three ratings: of importance to a subdiscipline, of national importance for a discipline, of international importance for a discipline. But essentially this is just a low level classification based on a quantification of who publishes, cites, and reads each journal.

Take-home message number 2: impact is relative. Compare multiple measures as presented by multiple institutions. Visibility to your subdiscipline is more important than overall visibility/impact.

So my paper might not be cited by many, and it might not be published in the highest impact journal, but it is a piece of work I am pretty pleased with and it seems to reach the few people around the world who have the same niche interests I have. Having many citations according to ISI in my discipline really does not mean much. Way more impressive is the number of views and downloads this paper gets on sites like Academia.edu. We publish our work because we want to share it with those who are interested, and we want to provoke discussion with the final aim to advance human knowledge. Who cares about high citation counts? Just make sure your paper is out there, freely available, actively promote it, send it to those who might be interested in discussing it with you. That’s what you want, not a high impact factor. All these numbers, and especially the Research Excellence Framework, make us forget sometimes that it is science we are doing.

(PS: as a young academic I realize my own career will be enhanced by playing this numbers game. I am sure it will, for now. But I also think things are changing with resources like Academia.edu, which will hopefully push entities with empty prestige like Science and Nature off their pedestals. Scientific quality control is not guaranteed by prestigious publishers, and there are other models of publishing that allow us to debunk bullshit science and keep the good bits)

MANIFESTO! for the study of ancient Mediterranean maritime networks

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MANIFESTO! Somehow I feel like this word should always be written in capitals and accompanied by an exclamation mark. I feel the same about the word REVOLUTION! I recently co-authored a manifesto for the first time, but the feeling was less revolutionary than I thought it would be. In November 2013 I attended a meeting at the University of Toronto, hosted by Justin Leidwanger and Carl Knappett. The meeting aimed to discuss network approaches to the study of maritime connectivity in the ancient Mediterranean. It brought together a group of archaeologists, historians and physicists working either in the Mediterranean or experienced with network approaches to the study of the past. An edited volume collecting all papers presented at this meeting is being prepared. But the key findings of our discussions were recently published in Antiquity+ as ‘A manifesto for the study of ancient Mediterranean maritime networks‘.

The manifesto has a very clear focus on the past phenomena that fall under the rather generic term ‘maritime connectivity’. A useful but simplifying definition of this term would be: ways in which people, places, and things separated by water were related. The manifesto makes methodological and theoretical suggestions that can be assembled into a research framework that will allow us to better understand past maritime connectivity. It is important to stress again that connectivity and past networks are referred to and treated in the manifesto as past phenomena, as things that actually happened or existed in the past. Although the authors see potential for approaches that conceptualise and formalise past connectivity as network concepts and data, it is not our main aim to understand these concepts and data. We hope to better understand the past social phenomena we are interested in, and we argue that network methods and theories offer some potential to help us do so. Two quotes from the manifesto (which raise discussion points I am particularly passionate about) should suffice to illustrate this focus: “formulating explicitly social questions should necessarily precede examination of spatial networks” and there is a “need to review critically our assumptions concerning the social function of maritime connectivity and the actors involved in these networks”.

The manifesto concludes by stressing the virtue of multi-vocality: there is no need for a single homogeneous maritime network studies approach. I believe this is a cautious and constructive attitude, in particular in light of the novelty of applying network methods and theories in our disciplines. We really have not yet discovered the full potential of these approaches for our disciplines. Until we have, we need to think and do creatively! And most importantly, evaluate critically and constructively! MANIFESTO!

The full manifesto is available for free on the Antiquity website.

In this oneoff, extended Project Gallery article, the participants of a recent workshop jointly present a manifesto for the study of ancient Mediterranean maritime connectivity. Reviewing the advantages and perils of network modelling, they advance conceptual and methodological frameworks for the productive study of seaborne connectivity. They show how progressive research methods can overcome some of the problems encountered when working with uneven datasets spanning large geographical regions and long periods of time. The manifesto suggests research directions that could better inform our interpretations of human connections, both within and beyond the Mediterranean. All references to the authors’ workshop papers in the text denote their oral presentations at the ‘Networks of Maritime Connectivity in the Ancient Mediterranean’ workshop held at the University of Toronto in November 2013.

The networks they are a-changin’: introducing ERGM for visibility networks

legosIn my madness series of posts published a few months ago I mentioned I was looking for a method to study processes of emerging intervisibilty patterns. I can finally reveal this fancy new approach to you 🙂 Here it is: introducing exponential random graph modelling (ERGM) for visibility networks. In previous posts I showed that when archaeologists formulate assumptions about how lines of sight affected past human behaviour, these assumptions imply a sequence of events rather than a static state. Therefore, a method is needed that allows one to test these assumed processes. Just analysing the structure of static visibility networks is not enough, we need a method that can tackle changing networks. ERGM does the trick! I just published a paper in Journal of Archaeological Science with Simon Keay and Graeme Earl that sets out the archaeological use of the method in detail. You can download the full paper on ScienceDirect, my Academia page or via my bibliography page. But in this blog post I prefer to explain the method with LEGOs 🙂

 

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Social network analysts often use an archaeological analogy to explain the concept of an ERGM (e.g., Lusher and Robins 2013, p. 18). Past material remains are like static snapshots of dynamic processes in the past. Archaeologists explore the structure of these material residues to understand past dynamic processes. Such snapshots made up of archaeological traces are like static fragmentary cross-sections of a social process taken at a given moment. If one were to observe multiple cross-sections in sequence, changes in the structure of these fragmentary snapshots would become clear. This is exactly what an ERGM aims to do: to explore hypothetical processes that could give rise to observed network structure through the dynamic emergence of small network fragments or subnetworks (called configurations). These configurations can be considered the building blocks of networks; indeed, LEGO blocks offer a good analogy for explaining ERGMs. To give an example, a network’s topology can be compared to a LEGO castle boxed set, where a list of particular building blocks can be used to re-assemble a castle. But a LEGO castle boxed set does not assemble itself through a random process. Instead, a step by step guide needs to be followed, detailing how each block should be placed on top of the other in what order. By doing this we make certain assumptions about building blocks and their relationship to each other. We assume that in order to achieve structural integrity in our LEGO castle, a certain configuration of blocks needs to appear, and in order to make it look like a castle other configurations will preferentially appear creating ramparts, turrets, etc. ERGMs are similar: they are models that represent our assumptions of how certain network configurations affect each other, of how the presence of some ties will bring about the creation or the demise of others. This is where the real strength of ERGMs lies: the formulation and testing of assumptions about what a connection between a pair of nodes means and how it affects the evolution of the network, explicitly addressing the dynamic nature of our archaeological assumptions.

More formally, exponential random graph models are a family of statistical models originally developed for social networks (Anderson et al. 1999; Wasserman and Pattison 1996) that aim to scrutinize the dependence assumptions underpinning hypotheses of network formation by comparing the frequency of particular configurations in observed networks with their frequency in stochastic models.

The figure below is a simplified representation of the creation process of an ERGM. (1a) an empirically observed network is considered; (1b) in a simulation we assume that every arc between every pair of nodes can be either present or absent; (2) dependence assumptions are formulated about how ties emerge relative to each other (e.g. the importance of inter-visibility for communication); (3) configurations or network building blocks are selected that best represent the dependence assumptions (e.g. reciprocity and 2-path); (4) different types of models are created (e.g. a model without dependence assumptions (Bernoulli random graph model) and one with the previously selected configurations) and the frequency of all configurations in the graphs simulated by these models is determined; (5) the number of configurations in the graphs simulated by the models are compared with those in the observed network and interpreted.

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My madness series of posts and the recently published paper introduce a case study that illustrates this method. Iron Age sites in southern Spain are often located on hilltops, terraces or at the edges of plateaux, and at some of these sites there is evidence of defensive architecture. These combinations of features may indicate that settlement locations were purposefully selected for their defendable nature and the ability to visually control the surrounding landscape, or even for their inter-visibility with other urban settlements. Yet to state that these patterns might have been intentionally created, implies a sequential creation of lines of sight aimed at allowing for inter-visibility and visual control. An ERGM was created that simulates these hypotheses. The results suggest that the intentional establishment of a signalling network is unlikely, but that the purposeful creation of visually controlling settlements is better supported.

A more elaborate archaeological discussion of this case study will be published very soon in Journal of Archaeological Method and Theory, so stay tuned 🙂 Don’t hesitate to try out ERGMs for your own hypotheses, and get in touch if you are interested in this. I am really curious to see other archaeological applications of this method.

References mentioned:

Anderson, C. J., Wasserman, S., & Crouch, B. (1999). A p* primer: logit models for social networks. Social Networks, 21(1), 37–66. doi:10.1016/S0378-8733(98)00012-4

Lusher, D., Koskinen, J., & Robins, G. (2013). Exponential Random Graph Models for Social Networks. Cambridge: Cambridge university press.

Lusher, D., & Robins, G. (2013). Formation of social network structure. In D. Lusher, J. Koskinen, & G. Robins (Eds.), Exponential Random Graph Models for Social Networks (pp. 16–28). Cambridge: Cambridge University Press.

Wasserman, S., & Pattison, P. (1996). Logit models and logistic regressions for social networks: I. An introduction to Markov graphs and p*. Psychometrika, 61(3), 401–425.

First Connected Past publication!

coverphotoAnna Collar, Fiona Coward and I started The Connected Past in 2011. Since then we have been enjoying organising a number of conferences, workshops and sessions together with our many friends in the TCP steering committee. Many collaborations and other fun things have followed on from these events but no publications yet, until now! Anna, Fiona, Claire and I recently published a paper in Nouvelles de l’archéologie. It was part of a special issue on network perspectives in archaeology edited by Carl Knappett.

Our paper’s aims are very similar to those of TCP in general: to communicate across communities of archaeologists and historians, to identify the challenges we face when using network perspectives, and to overcome them together. The paper first lists a number of challenges historians are confronted with, then a number of archaeological challenges. It argues how some of these challenges are similar and that it’s worth our while to collaborate. At the end of the paper we suggest a few ways of doing this. And it will be no surprise that one of the ways is to attend our future TCP events 🙂

You can download the full paper on Academia or via my bibliography page. You can read the abstract below.

The Connected Past will also publish a special issue of the Journal of Archaeological Method and Theory (first issue of 2015) and an edited volume (Oxford University Press, 2015). More about that later!

The last decade has seen a significant increase in the use of network studies in archaeology, as archaeologists have turned to formal network methods to make sense of large and complex datasets and to explore hypotheses of past interactions. A similar pattern can be seen in history and related disciplines, where work has focused on exploring the structure of textual sources and analysing historically attested social networks. Despite this shared interest in network approaches and their common general goal (to understand human behaviour in the past), there has been little cross-fertilisation of archaeological and historical network approaches. The Connected Past, a multidisciplinary conference held in Southampton in March 2012, provided a rare platform for such cross-disciplinary communication. This article will discuss the shared concerns of and seemingly unique challenges facing archaeologists and historians using network analysis techniques, and will suggest new ways in which research in both disciplines can be enhanced by drawing on the experiences of different research traditions.

The conference brought some common themes and shared concerns to the fore. Most prominent among these are possible methods for dealing with the fragmentary nature of our sources, techniques for visualising and analysing past networks – especially when they include both spatial and temporal dimensions – and interpretation of network analysis results in order to enhance our understanding of past social interactions. This multi-disciplinary discussion also raised some fundamental differences between disciplines: in archaeology, individuals are typically identified indirectly through the material remains they leave behind, providing an insight into long-term changes in the everyday lives of past peoples; in contrast, historical sources often allow the identification of past individuals by name and role, allowing synchronic analysis of social networks at a particular moment in time.

The conference also demonstrated clearly that a major concern for advancing the use of network analysis in both the archaeological and historical disciplines will be the consideration of how to translate sociological concepts that have been created to deal with interaction between people when the nodes in our networks are in fact words, texts, places or artefacts. Means of textual and material critique will thus be central to future work in this field.

Problems with archaeological networks part 3

spagThis third blog post in the series discusses space-related issues in archaeological network studies. As I mentioned before, I recently published a review of formal network methods in archaeology in Archaeological Review from Cambridge. I want to share the key problems I raise in this review here on my blog, because in many ways they are the outcomes of working with networks as an archaeologist the last six years. And yes, I encountered more problems than I was able to solve, which is a good thing because I do not want to be bored the next few years 🙂 In a series of four blog posts I draw on this review to introduce four groups of problems that archaeologists are faced with when using networks: method, data, space, and process. The full paper can be found on Academia.

The definition of nodes is not only dependent on data type categorisation but also necessarily reflects the research questions being asked, revealing an issue of spatial scales. Do the past processes we are interested in concern interactions between regions, sites or individuals? How will this be represented in node, tie and network definitions? The ability of network approaches to work on multiple scales is often mentioned as one of the advantages of using formal network methods (Knappett 2011). In practice, however, archaeological network analysts have traditionally focused on inter-regional or macro-scales of analysis. Knappett (2011) argues that it is on the macro-scale that network analysis comes into its own and a recently published edited volume reveals this regional emphasis (Knappett 2013). This insistence to work on large scales becomes quite unique in light of social network analysts’ traditional focus on individual social entities in interaction. SNA provides a multitude of good examples of how network methods could be usefully applied on a micro- or local scale of analysis (e.g. ego-networks). However, the nature of archaeological data, which rarely allows for individuals and their interactions to be identified with any certainty, should not be considered the only reason for this focus on the macro-scale. Arguably, networks lend themselves very well to exploring inter-regional interaction, and archaeologists have always had a particular interest in the movements and flows of people, resources and information across large areas. Moreover, many of the early applications of network methods in archaeology, which in some cases might have served as an example to more recent applications, concerned inter-regional interaction (e.g. Terrell 1976). One should acknowledge the importance of exploring how local actions give rise to larger-scale patterns if we are to benefit from the multi-scalar advantage of formal network methods (Knappett 2011).

It is not surprising that many archaeological network analysts are interested in exploring the dynamics between relational and geographical space (e.g. Bevan and Wilson 2013; Knappett et al. 2008; Menze and Ur 2012; Wernke 2012), given the importance of spatial factors in understanding archaeological data and archaeologists’ traditional interest in geographical methods (e.g. Hodder and Orton 1976). Despite early work by archaeologists on geographical networks (for an overview see chapter 2 in Knappett 2011), geographical space has been almost completely ignored by sociologists and physicists, resulting in a very limited geographical network analysis toolset for archaeologists to draw on (although see a recent special issue of the journal Social Networks [issue 34(1), 2012] and the review work by Barthélemy [2011], as well as techniques used in Space Syntax [Hillier and Hanson, 1984]).

References
Barthélemy, M. (2011). Spatial networks. Physics Reports, 499(1-3), 1–101. doi:10.1016/j.physrep.2010.11.002
Bevan, A., & Wilson, A. (2013). Models of settlement hierarchy based on partial evidence. Journal of Archaeological Science, 40(5), 2415–2427.
Hillier, B., & Hanson, J. (1984). The social logic of space. Cambridge: Cambridge University Press.
Hodder, I., & Orton, C. (1976). Spatial analysis in archaeology. Cambridge: Cambridge University Press.
Knappett, C. (2011). An archaeology of interaction: network perspectives on material culture and society. Oxford: Oxford University Press.
Knappett, C. (2013). Introduction: why networks? In C. Knappett (Ed.), Network analysis in archaeology. New approaches to regional interaction (pp. 3–16). Oxford: Oxford University Press.
Knappett, C., Evans, T., & Rivers, R. (2008). Modelling maritime interaction in the Aegean Bronze Age. Antiquity, 82(318), 1009–1024.
Menze, B. H., & Ur, J. a. (2012). Mapping patterns of long-term settlement in Northern Mesopotamia at a large scale. Proceedings of the National Academy of Sciences of the United States of America, 109(14), E778–87. doi:10.1073/pnas.1115472109
Terrell, J. E. (1976). Island biogeography and man in Melanesia. Archaeology and physical anthropology in Oceania, 11(1), 1–17.
Wernke, S. a. (2012). Spatial network analysis of a terminal prehispanic and early colonial settlement in highland Peru. Journal of Archaeological Science, 39(4), 1111–1122. doi:10.1016/j.jas.2011.12.014

Problems with archaeological networks part 2

Cuddly_Flying_Spaghetti_MonsterThis second blog post in the series discusses data-related issues in archaeological network studies. As I mentioned before, I recently published a review of formal network methods in archaeology in Archaeological Review from Cambridge. I want to share the key problems I raise in this review here on my blog, because in many ways they are the outcomes of working with networks as an archaeologist the last six years. And yes, I encountered more problems than I was able to solve, which is a good thing because I do not want to be bored the next few years 🙂 In a series of four blog posts I draw on this review to introduce four groups of problems that archaeologists are faced with when using networks: method, data, space, and process. The full paper can be found on Academia.

Network analysis is by no means a method devoid of any theoretical considerations. Most interestingly, theoretical critiques are often triggered by issues concerning the role of archaeological data. This is usually a result of the material nature of archaeological data serving as proxy evidence for past human behaviour, which poses a number of challenges.

Firstly, imposing categories and sometimes hierarchical relationships on data is a prerequisite for any network analysis. This results in the assumption that categories can actually be defined with any certainty (Butts 2009), and from the need to establish data categories ahead of the analysis, rather than letting them emerge from the analysis (Isaksen 2013). Indeed, the definition of nodes, ties and the network as a whole can be considered the most crucial phase of any archaeological network analysis. However straightforward such definitions seem, doing so in a critical manner is not as easy as it sounds. For example, we could choose to follow a formal ceramic typology, where each node represents a distinct type. When doing so we have to acknowledge that such typologies are modern constructs and that alternative categorisations can easily be developed. This in turn raises the issue that the network we analyse is not necessarily identical to the past networks we are trying to understand. For example, although in some cases it can be proven that particular ceramic types were used for particular purposes and in certain contexts, their meaning can nevertheless change through time, requiring a modification of our categorisation (van Oyen in press).

Secondly, unlike network analysts in many other disciplines, archaeologists work with primary data sources of a material nature. Social network analysts often only consider inter-personal interactions, whilst archaeological network analysts are forced to consider object-person and object-object interactions. A range of interactionist theoretical perspectives exist to confront materiality, and archaeological network analysts are faced with finding a workable framework that combines both network theories and methods (Knappett 2011).

In summary, the decisions archaeological network analysts make when defining nodes and edges, when selecting or modifying analytical techniques and when interpreting the outcomes, are fundamentally influenced by their theoretical preconceptions. There is not a single right way to incorporate and interpret archaeological data in network approaches.

References:
Butts, C. T. (2009). Revisiting the foundations of network analysis. Science, 325(5939), 414–6. doi:10.1126/science.1171022
Isaksen, L. (2013). “O What A Tangled Web We Weave” – Towards a Practice That Does Not Deceive. In C. Knappett (Ed.), Network analysis in archaeology. New approaches to regional interaction (pp. 43–70). Oxford: Oxford University Press.
Knappett, C. (2011). An archaeology of interaction. Network perspectives on material culture and society. Oxford – New York: Oxford University Press.
Oyen, A. Van. (n.d.). Networks or work-nets? Actor-Network Theory and multiple social topologies in the production of Roman terra sigillata. In T. Brughmans, A. Collar, & F. Coward (Eds.), The Connected Past: challenging networks in archaeology and history. Oxford: Oxford Univeristy Press.

Problems with archaeological networks part 1

Plate_of_SpaghettiAs I mentioned before, I recently published a review of formal network methods in archaeology in Archaeological Review from Cambridge. I want to share the key problems I raise in this review here on my blog, because in many ways they are the outcomes of working with networks as an archaeologist the last six years. And yes, I encountered more problems than I was able to solve, which is a good thing because I do not want to be bored the next few years 🙂 In a series of four blog posts I draw on this review to introduce four groups of problems that archaeologists are faced with when using networks: method, data, space process. The full paper can be found on Academia. This first blog post in the series discusses methodological issues, enjoy 🙂

Like any other formal techniques in the archaeologist’s toolbox (e.g. GIS, radiocarbon dating, statistics), formal network techniques are methodological tools that work according to a set of known rules (the algorithms underlying them). These allow the analyst to answer certain questions (the network structural results of the algorithms), and have clear limitations (what the algorithms are not designed to answer). This means that their formal use is fundamentally limited by what they are designed to do, and that they can only be critically applied in an archaeological context when serving this particular purpose. In most cases, however, these formal network results are not the aim of one’s research; archaeologists do not use network methods just because they can. Instead one thinks through a networks perspective about the past interactions and systems one is actually interested in. This reveals an epistemological issue that all archaeological tools struggle with: there is a danger that formal networks are equated with the past networks we are trying to understand (Isaksen 2013; Knox et al. 2006; Riles 2001). In other cases, however, formal analysis is avoided altogether and concepts adopted from formal network methods are used to describe hypothetical past structures or processes (e.g. Malkin 2011). Although this sort of network thinking can lead to innovative hypotheses, it is not formal network analysis (see reviews of Malkin (2011) by Ruffini (2012) and Brughmans (2013)). However, such concepts adopted from formal network methods often have a very specific meaning to network analysts and are associated with data requirements in order to express them. Most crucially, when the concepts one uses to explain a hypothesis cannot be demonstrated through data (not even hypothetically through simulation), there is a real danger that these concepts become devalued since they are not more probable than any other hypotheses. Moreover, the interpretation of past social systems runs the risk of becoming mechanised when researchers adopt the typical interpretation of network concepts from the SNA or physics literature without validating their use with archaeological data or without modifying their interpretation to a particular archaeological research context. This criticism is addressed at the adoption of formal network concepts only. It should be clear that other theoretical concepts could well use a similar vocabulary whilst not sharing the same purpose or data requirements, in which case I would argue to refrain from using the same word to refer to different concepts or explicitly address the difference between these concepts in order to avoid confusion.

Although it is easy to claim that the rules underlying formal network techniques are known, it is less straightforward to assume that the traditional education of archaeologists allows them to decipher these algorithms. Archaeologists are not always sufficiently equipped to critique the mathematical underpinnings of network techniques, let alone to develop novel techniques tailor-made to address an archaeological question. For many archaeologists this means a real barrier or at least a very steep learning curve. Sadly, it also does not suffice to focus one’s efforts on the most common techniques or on learning graph theory. Like GIS, network analysis is not a single homogeneous method: it incorporates every formal technique that visualises or analyses the interactions between nodes (either hypothetical or observed), and it is only the particular nature of the network as a data type that holds these techniques together (Brandes et al. 2013). For this purpose it draws on graph theory, statistical and probability theory, algebraic models, but also agent-based modelling and GIS.

A thorough understanding of the technical underpinnings of particular network techniques is not an option; it is a prerequisite for a critical interpretation of the results. A good example of this is network visualisation. Many archaeologists consider the visualisation of networks as graphs a useful exploratory technique to understand the nature of their data, in particular when combined with geographical visualisations (e.g. Golitko et al. 2012). However, there are many different graph layout algorithms, and all of them are designed for a particular purpose: to communicate a certain structural feature most efficiently (Conway 2012; Freeman 2005). These days, user-friendly network analysis software is freely available and most of it includes a limited set of layouts, often not offering the option of modifying the impact of variables in the layout algorithms. Not understanding the underlying ‘graph drawing aesthetics’ or limiting one’s exploration to a single layout will result in routinized interpretations focusing on a limited set of the network’s structural features.

Archaeologists who consider the application of network methods to achieve their research aims must be able to identify and evaluate such issues. Multi-disciplinary engagement or even collaboration significantly aids this evaluation process.

References:
Brandes, U., Robins, G., McCranie, A., & Wasserman, S. (2013). What is network science? Network Science, 1(01), 1–15. doi:10.1017/nws.2013.2
Brughmans, T. (2013). Review of I. Malkin 2011. A Small Greek World. Networks in the Ancient Mediterranean. The Classical Review, 63(01), 146–148. doi:10.1017/S0009840X12002776
Conway, S. (2012). A Cautionary Note on Data Inputs and Visual Outputs in Social Network Analysis. British Journal of Management. doi:10.1111/j.1467-8551.2012.00835.x
Freeman, L. C. (2005). Graphic techniques for exploring social network data. In P. J. Carrington, J. Scott, & S. Wasserman (Eds.), Models and methods in social network analysis (Vol. 5, pp. 248–268). Cambridge: Cambridge University Press. doi:10.3917/enje.005.0059
Golitko, M., Meierhoff, J., Feinman, G. M., & Williams, P. R. (2012). Complexities of collapse : the evidence of Maya obsidian as revealed by social network graphical analysis. Antiquity, 86, 507–523.
Isaksen, L. (2013). “O What A Tangled Web We Weave” – Towards a Practice That Does Not Deceive. In C. Knappett (Ed.), Network analysis in archaeology. New approaches to regional interaction (pp. 43–70). Oxford: Oxford University Press.
Knox, H., Savage, M., & Harvey, P. (2006). Social networks and the study of relations: networks as method, metaphor and form. Economy and Society, 35(1), 113–140. doi:10.1080/03085140500465899
Malkin, I. (2011). A small Greek world: networks in the Ancient Mediterranean. Oxford – New York: Oxford University Press.
Riles, A. (2001). The Network inside Out. Ann Arbor, MI: University of Michigan Press.
Ruffini, G. (2012). Review of Malkin, I. 2011 A Small Greek World: Networks in the Ancient Mediterranean. American Historical Review, 1643–1644.

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