CAA 2011

This page is dedicated to the session at CAA 2011 titled ‘Connecting the dots: critical approaches in archaeological network analysis’. Here you will find the introduction, list of contributors, their papers, as well as post-conference reviews.


Introduction
Summary

Contributors

Abstracts

Introduction

Relationships matter. They did in the past and they do in the present. If we want to understand the structure of our datasets, the particular actions of past individuals, or the properties of past large-scale processes, the explicit study of relationships is crucial. And this can be done through a networks perspective.

In recent years the network as a research perspective and as a set of analytical techniques has become more popular in archaeology. This thanks to the work of people like Carl Knappett, Tim Evans, Ray Rivers, Fiona Coward, Clive Gamble, Shawn Graham, Leif Isaksen, Alexander Bentley, Herbert Maschner, Stephen Shennan, Cyprian Broodbank, Jessica Munson, Martha Macri, Koji Mizoguchi, Søren Sindbæk, and others. These archaeological applications have been influenced by a similar rise in the cultural anthropological and historical work of scholars like Irad Malkin, Anna Collar, John Terrell, Johannes Preiser-Kapeller, John Padgett, Paul McLean, Paul Ormerod, Andrew Roach and Christopher Ansell.

The network is a research perspective, if anything. It is not a homogeneous method as titles like ‘social network analysis’ suggest. Rather, it should be seen as a set of ideas, techniques and applications sharing some key assumptions. A first assumption states that the relationships between entities (like people, objects or ideas) matter and that these should be examined if we are to understand the behaviour of these entities. The importance of relationships implies a second assumption: the whole is greater than the sum of its parts. Through the interactions of entities collective behaviour emerges that cannot be understood merely through the study of entities in isolation.
Network analysis provides a scientific framework to examine relationships and their effects directly. It allows archaeologists to bridge the gap between the reductionist study of parts and the constructionist study of the related whole as Bentley and Maschner put it (2003, 1). These allow for two main fields of applications in archaeology: exploring complex datasets in data-rich environments and examining past complex systems.

Two scientific traditions have been particularly influential to archaeologists. Firstly, social network analysis, which focuses exclusively on social entities. Secondly, complex networks in physics, or what has been termed as the “new” science of networks. Archaeologists cannot adopt network techniques and applications from these traditions directly and uncritically, however. The nature of archaeological data makes the direct identification of social entities, like past individuals and communities, and how they related problematic. Similarly, archaeological data, which are typically material reflections of particular actions performed by individuals or groups of individuals, force archaeological attempts of identifying emergent self-organising properties in past complex systems to be rooted in individual-level data.

This session aims at confronting such issues. I am delighted that so many scholars from different disciplines with original applications of a networks research perspective have answered to the call for papers. It is hoped that confronting these diverse applications will reveal the issues as well as the potential of networks in archaeology. I sincerely hope that this session will give rise to multi-disciplinary discussions and collaboration. This is a necessity if we want to take our work beyond the current generation of network-based approaches, if we want to do more than the mere identification and description of emergent-properties or the structure of connectivity. Rather, we should aim to think through a networks research perspective, to acknowledge the implications of imposing the ‘network’ as a modern concept on past phenomena, and to explain the identified structures through re-contextualisation, the confrontation of the local with the global and explicit archaeological reasoning and data critique.
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Summary

The first presentation of the day was by Maximilian Schich and Michele Coscia talking about ‘Untangling the Complex Overlap of Subject Themes in Classical Archaeology’.

Maximilian and Michele used the Archäologische Bibliographie, a library database consisting of over 450.000 titles, 45.000 classifications, and 670.000 classification links. They looked at the co-occurrence of classifications, creating networks where two classifications are connected if they appear in the same book as well as networks where classifications are connected when the same author writes about them. Using whatever database software you can look at the local level of this massive dataset. This was not of interest to the authors. In stead, Max and Michele looked at the bigger picture. They devised a method that allowed them to explore the dataset on three different scales: the local level (database level), the meso-level and the global level. On the global level they were able to identify academic communities, but also clusters of communities (so communities of communities). They also looked in detail at how these communities evolved over time. On the meso-level they threshold the data based on co-occurrence and significance, which produced interesting results. Max and Michele concluded that this approach to academic literature allows us to look at the fine-grained structure of how archaeology actually works. Their three-level method using hierarchical link clustering and association rule mining made it blatantly clear that complex overlaps are everywhere in academia!

Questions: Guus Lange asked what type of clustering was applied, to which Max responded that no clustering was performed on nodes but on the links. Graeme Earl asked how the classifications were derived from the database and whether they thaught about exploring how the classifications themselves grew and transformed. Max replied that there is no limit to the number of books per classification but there is a sharp limit to the number of classifications there are per book. What is interesting, he said, is that we nevertheless get this big picture. Tom Brughmans wrapped up with a final question about how long it took them to do this work. Michele and Max mentioned that it took them one year but once the workflow is engineered it could be done in two weeks time.

Diego Jimenez was our next speaker. He presented on ‘Relative Neighborhood Networks for Archaeological Analysis’.

Diego is interested in archaeological attempts to find meaningful spatial structure between archaeological point data. He relies on graph theory to find structure based purely on the spatial distribution of points and suggests objective ways of analysing connections between them. In his talk Diego focused mainly on the methodology rather than any specific applications. Rather than nearest neighbour approaches, he suggested a relative neighbourhood concept as the basis for his method. Two points are relative neighbours if the regions of influence drawn around this pair does not include other points. Graphs can be constructed using this concept. Most interestingly, Diego mentioned that a parameter beta can be included to change the regions of influence. This allows for a series of graphs to be created with different levels of connectivity. Diego suggested some space syntax approaches to analysing these graphs including graph symmetry, relative asymmetry and distributedness.

Questions: Maximilian Schich was interested in how control was defined in Diego’s analysis of the graphs and mentioned that peripheral nodes might often have a high level of control in a network. Diego mentioned that these are indeed important patterns that need to be acknowledged by archaeologists and his method would be a way to be sensitive to them.

After Diego we had the honour of listening to a historians experiences with network analysis. Johannes Preiser-Kapeller talked about ‘Networks of border zones – multiplex relations of power, religion and economy in South-eastern Europe, 1250-1453 CE’.

Johannes’ paper made it very clear that, although archaeologists can rarely obtain datasets of such quantity or quality as in other disciplines, we still have sources that inform us of different types of relationships for which a networks approach can lead to highly interesting results. He constructed five distinct networks from different data types (streets, coastal sea routes, church administration, state administration, participants of the 1380 synod) some of which were compared for three different moments in time (1210, 1324, 1380). Initially some general measurements, like average distance, clustering coefficient and density, are used to explore the topology of individual networks, as well as compare between networks of different sources. Secondly the overlap of groups of related nodes is identified to explore the correlation between different networks. Johannes then merged all these networks to create what he considers a multiplex representation of frameworks of past human interactions. Thirdly, the combined effects of the multiplex network on the topology of social interaction, as illustrated through the participants in the 1380 synod, is explored. He concluded by stating that this framework that emerged from different sources might be more than merely the sum of its parts. In short, even though we are dealing with fragmentary and limited datasets, applying a networks perspective explicitly might still guide us to highly interesting and surprising results.

Mihailo Popovic presented the final paper before lunch. His talk titled ‘Networks of border zones – a case study on the historical region of Macedonia in the 14th century AD’ was strongly related to that of his colleague and fellow historian Johannes.

Mihailo’s paper explored the border zone between the Byzantine empire and the emerging Serbian state in the 14th century AD. His case-study focused on the area of the city Stip and the valley of the river Strumica. Four central places were identified in the valley on the basis of written medieval sources: the towns of Stip, Konce, Strumica, and Melnik. Mihailo is interested in understanding how these places interact with each other. For example, can an exclusive relationship between the central places and the surrounding smaller settlements be assumed? Or did all settlements interact equally with each other? Mihailo stresses the importance of evaluating the landscape on the ground to explore how this might have influences urban interactions. Based on Medieval written sources that identify the larger settlements as religious, administrative and economic centres, he argues for an exclusive relationship of the larger towns with the smaller ones. This leads to astral-shaped networks. Mihailo’s analysis shows that Strumica has the highest closeness centrality value, whilst Stip has the highest betweenness value. To conclude he stressed the wider questions that his networks approach leave open: is the settlement pattern complete? Is the network realistic in view of the landscape? Is the networks’ astral-shape justifiable or did the villages also interact with each other? May we assume interactions between other villages? How to integrate human behaviour?

We reconvened in the afternoon to listen to Ladislav Smejda talking about ‘Of graphs and graves: towards a critical approach’

Ladislav discussed the artefact distributions from a cemetery dated around 2000 BC. He explored eleven attributes consisting of grave dimensions and the presence or absence of grave good categories, which can appear in many combinations. Ladislav limited the relationships of co-presence of grave goods to statistically significant correlations, which resulted in a graph representing his eleven attributes and relationships of positive and negative correlations between them. He then moved on to divide the graph into two substructures. Substructure A is defined by correlations between ornaments (faience beads, bone beads, hair ornaments) and grave depth. Substructure B includes stone artifacts, cattle ribs and grave length. These two sets seem to show strongly different patterns, which can be explored as networks. Simple networks were created based on the presence or absence of artefacts significant to either substructure A or B, showing different structured. Secondly, Ladislav introduced the concept of the hypergraph where the edges are more like areas in which more than one node can be included. Ladislav concludes that a graph theory and network analysis approach is useful to handle, visualise, and explore the structure of archaeological datasets, whilst leaving plenty of options open to take the analysis further with different tools (like GIS).

Questions: Ladislav’s presentation sparked many questions, partly because we had plenty of time in the afternoon due to serious changes in the conference schedule. So I decided to transcribe the questions as a simple dialogue.

Leif Isaksen: what does the negative correlation mean? That the attributes don’t occur together?

Ladislav: they don’t appear together with statistical significance.

Maximilian Schich: what’s the negative correlation with grave depth and faience beads?

Ladislav: deep graves have bone beads and shallow graves tend to have faience beads.

Leif Isaksen: how has the grave depth been recorded?

Ladislav: data was taken from excavation reports. There is no specification of how they measured that. The whole site was excavated by a single person. Possibly grave depth was measured from the top soil downwards.

Maximilian Schich: You could compare every link in this diagram, maybe as an XY diagram where you have bone beads vs grave depth for example. Do you know how many bone beads there are? How many graves? Are these measured just as presence/absence or as real counts?

Ladislav: There are 70 graves with bone beads, and 430 graves in total. I tried both approaches but presence/absence is better because in many cases it was impossible to count precise numbers. I don’t think it is important to know how many bone beads they had exactly.

Maximilian Schich: so you could draw an XY diagram. If you only have 430 graves it’s very easy to draw a histogram. And instead of the correlation you could give us all the data points.

Ladislav: I did all these things. At the moment I have so many outputs of this data that it could not be presented in a 15 minute paper. Clearly there is much more you could with this data.

Maximilian Schich: how can you assign grave depth to a region where there is no grave?

Ladislav: the grey background is just an interpolation of the grave points. The crucial thing this shows is that there are no deep graves on one end of the matrix and no shallow graves on the other.

Diego Jimenez: is there any significance in the distribution of objects within each grave, and is that relevant for the analysis.

Ladislav: it’s recorded, I tried to follow this up but not with graph theory.

Diego Jimenez: this is what sparked my interest in using graphs, as I used it to understand the spatial distribution of artefacts within graves. The spatial arrangement might have a symbolical importance.

Tom Brughmans: it’s a good example of a network within a network as well.

Leif Isaksen: it would be great to see these graves’ locations projected in geographical space, did you pursue a geographical approach as well?

Ladislav: yes, but that is the topic of another presentation.

Tom Brughmans: I am not sure if the statistics used to explore correlations are necessary, because these correlations might just emerge when exploring the co-presence of different types of artefacts as a network.

Ladislav: the presence/absence is exactly what is represented, so it is a different way of achieving the same thing.

Maximilian Schich: you have enough data but not too much to prevent a real networks visualisation. There is no need to reduce your data to a few nodes and links. All your data can be shown on one graph and a few histograms.

Ladislav: I did not do this because I am looking for the simplest possible structure, in the simplest possible representation.

Due to the changes in the conference schedule the afternoon also saw two unscheduled presentations by Leif Isaksen and myself being added to the network analysis session.

Tom Brughmans presented a paper titled ‘Facebooking the Past: a critical social network analysis approach for archaeology’.

I started out with a short fiction about how Cicero became consul of Rome thanks to Facebook and Twitter. Obviously, that is not the story we will find in the history books. But by making the analogy between modern ideas of social networks and past social processes it becomes clear what it is we are actually doing when using social network analysis. I argued that there are three issues related to the archaeological (and indeed historical) use of social network analysis. Firstly, that the full complexity of past social interactions is not reflected in the archaeological record, and social network analysis does not succeed in representing this complexity. Secondly, that the use of social network analysis as an explanatory tool is limited and it implies the danger that the network as a social phenomenon and as an analytical tool are confused. Thirdly, human actions are based on local knowledge of social networks, which makes the task of deriving entire past social networks from particular material remains problematic. To confront these issues I argued to turn the network from the form of analysis to the focus of analysis and back again in an integrated analytical process drawing upon ego-networks, complex real-world network models and affiliation networks approaches.

Discussion: the questions about this paper changed into a fascinating discussion about the nature of archaeological and historical data and how this influence our use of network techniques.

Maximilian Schich: I think that indeed data from today is different than from the past but only because more is different. In a sense I think it cannot be justified to say that we should not look for social networks because the data is incomplete. Modern day data, like mobile phone record for example, are also incomplete. Facebook does not cover all social interactions. One topic that has been mentioned a lot today is that of multiplex networks. There is a conceptual danger with this because it assumes that we can discretize between different types of networks, whilst actually that is not possible. When collecting data there is one thing that is definitely different from data like mobile phone networks for example, which is the multiplicity of opinion. If you collect something and I collect something the data will look completely different. All these things are complicated, a lot of time needs to be invested in this, I agree that we have to work with what we have. But we should not capitulate in front of this problem saying that it’s perfectly fine to just bullshit theoretically because the data is unavailable.

Tom Brughmans: I agree that archaeological data is not necessarily any different than data sociologists or physicists use, like mobile networks for example. Another example is e-mail communication. A sample of this type of social interaction might be limited because some people were out of office whilst you were taking the sample, and it is also an indirect reflection of social relationships as we explore the e-mail directly but not the people. So our data might not be different. But what possibly makes archaeology (and other historical disciplines) different is that all our theory is geared towards this issue. We are very aware that we are dealing with indirect fragmentary samples to explore dynamic processes in the past. Whilst in other disciplines scholars might over simplify this issue, in the historical disciplines we are very aware of it and cannot avoid it. Another difference might be what you said that when different people excavate the same thing, different data will emerge. But more crucial I think is that after collection the data is actually destroyed, it is not a repeatable test. The data only lives on in a structure that makes sense to the person who collected it. So given these two issues I think archaeological applications of social network analysis can be different from other disciplines.

Yasuhisa Kondo: Just a comment. I believe that social networking like Facebook and Twitter is also changing archaeologists’ behaviour. When I was in Oman a few months ago, for example, the Middle East crisis was picking up and I was informed about the situation of Egyptian heritage through social networks. Secondly, in Japan we use Facebook to collect data. So it is interesting to see that it is not only useful to think about present social relationships between archaeologists but also about past social networks.

Johannes Preiser-Kapeller: when comparing modern complex network analysis in physics and historical network analysis, in physics scholars don’t want to just analyse but they also want to explain, to understand the mechanism that makes the network function. They generate ideas on how such network actually worked, like through preferential attachment for example. We do not know if networks in the past actually worked in the same way, if such mechanisms can be imposed on historical networks. Our data sometimes isn’t even large enough to identify degree distributions that reveal power laws for example.

Tom Brughmans: I am glad that you bring this up because I have been struggling with a similar issue. Do these real-world network emergent properties actually explain anything. Aren’t they just a description of a complex network structure, of how it evolves rather than explain the network. The descriptive aspects of such models can easily be applied to historical data, when we accept the assumption that the whole is greater than the sum of its parts and complexity arises from local interactions. But it does not really explain much does it.
Johannes Preiser-Kapeller: modern complex network models assume that they are not merely descriptive but they are laws that explain how things like social relationships functioned. It’s more than description, they are looking for mechanisms. The question is if we can also identify such mechanisms for past networks which can help us to explain how social interaction worked.

Maximilian Schich: concerning the power-law thing, preferential attachment is only one of thousands of mechanisms which can result in a power law. And in some cases it can not even be proven that the power law is there because of a lack of data. So we cannot blame the people that came up with the idea of preferential attachment in the first place as if they assumed that it explained all power laws. It is not their fault that they got cited 60.000 times. We should acknowledge that this is just one model that actually works, and it explains a lot, just like the small-world model. But both of them are incomplete. Concerning historical networks: I think it is a big mistake of historians or other scholars in humanities to think that we are special, cause we are actually not. Of course we have different documentation and different numbers. But the underlying approach of hypothesis testing and of saying “let’s look at what structure the data has”, that is the approach complex network scientists have. They do not assume a universal law. This is the same approach taken in the humanities.

Mihailo Popovic: many people are not aware of the exact historical situation. Like 14th centure Byzanthium for example: 90% of the population lived in vilages, the flow of information does not exist on an international level it is a local thing.

Maximilian Schich: are you sure?

Mihailo Popovic: I am sure, based on the sources we have. Thirdly, there are slaves in the villages who’s movement is restricted. Finally, Illiteracy is immense. To come to my point: we have written sources that are written by 5% of the population, if even that. And of those perhaps 20% percent survive. So what do we do? We cannot just assume that comparing a dataset of six million people communicating over the internet with a historical dataset like the one I described can be done through the same approach. We have to face the reality of the historical period. It took us a lot of time and effort to collect these relatively small and still fragmentary datasets.

Maximilian Schich: but we can agree that things are being spread between people, even if they are not aware of it. Information can spread in the same way electricity spreads for example, electrons push other electrons along, not every electron goes all the way from Europe to China. We have such a situation where we can assume that some information was spread for most periods in the past. So to say that there are individuals who are immobile and construct sampling boundaries based on that, I don’t think such a strict limitation can work.

Johannes Preiser-Kapeller: of course, there was some kind of globalization already in the 14th century, there was some connection which even reached villages. It would be perfect if we could paint a picture of such a global system. We can do it on a superficial level, but we do not have the necessary sources to go in more depth. A prosopographic database of the Byzantine period, for example, contains 30.000 people. Of those, 80% were clerics and not more than 200 were farmers. We can see what is going on for the top 5% of the people, and we can see the mechanisms like preferential attachment working on this level. But we are still struggling with the artificial border created by our data, as you mentioned. We do not have the entire system. This sample problem will always be there in the historical discipline.

Maximilian Schich: that’s exactly the same problem as we have in any other discipline. It is not a history or non-history problem but a percolation problem. Physicists working on percolation have to come up with a solution and then we can make an educated gues of how much of the system we have.

Johannes Preiser-Kapeller: let me give you another example. When I showed my work to Stephan Thurner in Vienna, who worked on a massive dataset of 300.000 individuals interacting through a computer game, he said my dataset of only 200 aristocrats is not enough. If you do not have at least 1000 individuals you cannot identify any mechanism, you need statistical significance. So this is a limit imposed on historical disciplines in applying interesting mechanisms identified in complex real-world networks.

After the discussion we still had the pleasure to listen to Leif Isaksen talking about ‘Lines, Damned Lines and Statistics: Unearthing Structure in Ptolemy’s Geographia’. Sadly my tape recorder died at this point, so here is Leif’s abstract rather than a review.

Ever since the rediscovery of Ptolemy’s Geographia in 1295, scholars have noted that it is troublingly inconsistent both internally and with the environment in which it was supposedly compiled. The problem for analysts to overcome is that the catalogue has been corrupted, amended and embellished throughout its history. It is therefore imperative to find more robust means to look for structural trends. Recent publications of the theoretical chapters and a digital catalogue of coordinates provide a variety of new possibilities. We are not alone in advocating computational procedures but will discuss two techniques that do not appear to have been considered in the literature so far and the conclusions they appear to give rise to.

First, statistical analysis of the coordinates assigned to localities demonstrates clearly that ostensible precision (whether to the nearest 1/12, 1/6, 1/4, 1/3 or 1/2 degree) varies considerably by region and feature type and is locally heterogeneous. In other words, the composite nature of the data cannot only be confirmed, but we can build a clearer picture of how the sources varied by area. Secondly, while many studies have addressed either the point data or the finished maps, simple linear interpolation between coordinates following the catalogue provides a unique insight into the ‘invisible hand’ of the author(s). The unmistakable stylistic families that emerge, and the occasionally arbitrary limits imposed on them, provide further important evidence about the catalogue’s internal structure.
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Contributors

Tom Brughmans (University of Southampton, UK)
Michele Coscia (Università di Pisa, Italy)
Leif Isaksen (University of Southampton, UK)
Diego Jimenez (National Institute of Anthropology and History, Mexico)
Mihailo Popovic (Austrian Academy of Sciences, Austria)
Johannes Preiser-Kapeller (Austrian Academy of Sciences, Austria)
Maximilian Schich (Northeastern University, US)
Ladislav Smejda (University of West Bohemia, Czech Republic)
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Abstracts

Session abstract
Tom Brughmans

This session aims at confronting existing practical and theoretical applications of network analysis, in order to stimulate and enrich future critical archaeological network-based methods.

Network analysis methods are applied in an ever larger number of disciplines, thanks to the increasing complexity of data used in most disciplines and these methods’ ability to analyse complex relationships directly. Network analysis approaches are also increasingly popular in the archaeological discipline, as relationships are omnipresent in archaeological data and interpretation. It is used to explore archaeologically attested structures, to understand known relational behaviour and to simulate archaeological hypotheses. Many archaeological applications are strongly influenced by network techniques developed in other disciplines, most prominently physics and social network analysis. However, given the specific nature of archaeological data (e.g. as proxy evidence) and the many issues surrounding it (e.g. sampling), such techniques cannot be adopted into the archaeological discipline without question. Moreover, this is largely a one-way adoption, with some ideas being extremely influential to archaeologists but few archaeological network approaches being shared within the archaeological discipline.

This session wishes to address the absence of a shared body of influential archaeological network techniques. It will try to take the first step in overcoming this issue by bringing together existing diverse and inter-disciplinary network analysis applications, and identifying methodological and theoretical common grounds. By confronting these applications and exploring their diverse archaeological potential, some ideas of what future critical archaeological network analysis techniques will look like might emerge.

This session invites inter-disciplinary papers that combine practical computational examples (not necessarily archaeological) with theoretical reflections on archaeological network analysis. Collaborative working towards a shared set of computational techniques and theoretical guidelines for future critical applications of archaeological network analysis should be the key feature of the papers in this session.

Untangling the Complex Overlap of Subject Themes in Classical Archaeology
Maximilian Schich & Michele Coscia
In this talk we try to untangle the complex overlap of subject themes in Classical Archaeology , taking a closer look at the network of subject co-popularity in Archäologische Bibliographie , a database that records and classifies scholarly literature since 1956. Like other traditional literature databases, Archäologische Bibliographie is based on a relatively static tree of about 45.000 subject headings that is characterized by a controlled growth in certain areas, such as in alphabetic lists of sites or persons. After more than 50 years of use, the relatively static nature of the tree poses the question, if it still reflects the structure of the discipline of Classical Archaeology. In previous work ( Schich et al. 2009 ) it has been shown that we can approach the emerging structure of the field by looking at the network of co-popular subject themes, i.e. the network of subject themes occurring together in scholarly publications. Unfortunately the network of subject co-popularity turns out to be void of an intuitive higher-level community structure, as opposed to the clear cut a priori definition of the tree of subject headings. As we have shown before, key concepts in the network of co-popularity are highly overlapping in a complex way, making it very difficult to spot any sharp bounded sub-discipline in Classical Archaeology.

In order to solve this problem of entanglement, we devise a strategy of exploring the data using a number of methods. First, we run a Hierarchical Link Clustering method ( Ahn et al. 2010 ) on the weighted and intelligently filtered co-popularity network, in order to extract and visualize a hierarchical set of overlapping communities. Second, we refine the outcome with a link specificity measure that we create using an algorithm that mines for so called association rules (cf. Agrawal Srikant 1994 ). Finally we create a browsable set, in which the user can navigate up and down from the global to the most granular level, exploring the structure and evolution of Classical Archaeology as it emerges from the local activity of its contributors.

Bibliography:

M. Schich, C. Hidalgo, S. Lehmann, and J. Park: The Network of Subject Co-Popularity in Classical Archaeology. To appear in the first issue of Bolletino di Archaeologia On-line. [accepted in 2009, preprint online]. URL: http://archiv.ub.uni-heidelberg.de/artdok/volltexte/2009/715/

Y.-Y. Ahn, J. P. Bagrow, and S. Lehmann: Link communities reveal multi-scale complexity in networks. Nature, 466:761, 2010.

R. Agrawal and R. Srikant: Fast Algorithms for Mining Association. Proc. 20th Int. Conf. on Very Large Databases (VLDB 1994, Santiago de Chile), 487-499

Relative Neighborhood Networks for Archaeological Analysis
Diego Jimenez
In many archaeological projects one is given the spatial coordinates of a set of points (e.g. sites, artifacts) and it is desired to find significant connections among the points to produce a graph or network structure that becomes perceptually meaningful in some sense. In the case of archaeological sites, for example, we may want to identify links among settlements to isolate groups that are closely related by virtue of their spatial arrangement. In a large area, this may indicate the presence of sub-regional organizations. We may also want to identify those sites that exercise more control over the network. Alternately, we may want to measure how segregate of integrated each settlement is within the regional organization.

This paper presents a new method to derive the network purely from the spatial coordinates of the points and suggests objective ways to analyze the connections among the points. The method is based on the graph theoretical notion of relative neighborhood.

As it names suggests, the concept of relative neighborhood captures the idea of points being “relatively close”, “relatively related” o “relatively associated”, which contrasts with rigid notions based on absolute distances, such as “nearest neighbor” or farthest away relations. This has applications in situations where we need to establish contextual relationships of one point, say x, with several adjacent points. In other words, when we want to determine whether x has other significant spatial associations besides its nearest neighbor.

The notion of relative neighborhood assumes a “region of influence” attached to pairs of points. The size and shape of such region is determined by the relative separation among all possible pairs of points, and therefore the connections are dependent on the spatial configuration of point set.

We demonstrate that the notion of relative neighborhood is more flexible and useful to answer archaeological questions.

The analysis of “relatively close” points dates back to 1969 when Lankford (1969) defined the concept mathematically. Then, others developed the idea further (Gabriel and Sokal 1969; Toussaint 1980; Urkuhart 1982; Kirkpatrick and Radke 1985). These efforts produced a range of adjacency graphs such as the Relative Neighborhood Graph, the Gabriel Graph, the Beta-skeletons, the Limited Neighborhood Graph, etc.

Since 1998, we have been applying the notion to a wide range of archaeological applications and have produced a method specifically tailored to our discipline.

References:

Gabriel, K. R., and R. R. Sokal. 1969. A new statistical approach to geographic variation analysis. Systematic Zoology, 18: 259-278.

Lankford, P. M. 1969. Regionalization: theory and alternative algorithms. Geographical Analysis, 1: 196-212.

Kirkpatrick, D. G., and J. D. Radke 1985. A framework for computational morphology. In Computational Geometry, ed. G. Toussaint, 217-248. North Holland: Elsevier Science Publishers.

Toussaint, G.T. 1980a. The relative neighbourhood graph of a finite planar set. Pattern Recognition, 12: 261-268.

Urquhart, R. 1982. Graph theoretical clustering based on limited neighbourhood sets. Pattern Recognition, 15 (3): 173-187.

Networks of border zones – a case study on the historical region of Macedonia in the 14th century AD
Mihailo Popovic
The present case study derives from research conducted within the FWF – Austrian Science Fund project on the ‘Economy and regional trade routes in northern Macedonia (12th-16th century)’ (project P 21137-G19) under the supervision of Prof. Dr. Johannes Koder, which forms part of the overall project Tabula Imperii Byzantini (TIB) of the Austrian Academy of Sciences on the historical geography of the Byzantine Empire.

This case study puts an emphasis on the border zones between the aforesaid empire, which existed from the 4th century AD until the 15th century AD and stretched over vast parts of the central as well as eastern Mediterranean, and the emerging Serbian mediaeval state in the historical region of Macedonia in South-eastern Europe in the 14th century AD. At first, it evolves on a macro-level of historical geography by taking into account the treaty between Charles Count of Valois (1270-1325) and the Serbian king Stefan Uros II Milutin (ca. 1253-1321) on the partition of areas of influence in the case of the former’s reestablishment of the Latin Empire in Constantinople. The treaty was concluded in the year 1308 AD and in accordance with its dispositions, the Serbian king should have received territories in and around Deber (Debar), Prilep (Prilep), Prisec (Prosek), Ouciepoullie (Ovce Pole) as well as Stip (Stip). This geographical outline enables us to identify the approximate border zones between the Byzantine Empire and the Serbian state in South-eastern Europe at that time.

In order to comply with four practical issues expressed by Wagstaff in respect of network analysis and logistics, the present case study turns to the micro-level of historical geography by focusing on one of the areas of the macro-level, namely the city of Stip (Stip) and consequently on the adjacent valley of the river Strumica (Strumesnica), which lies today in the south-eastern part of the Former Yugoslav Republic of Macedonia as well as in the south-western part of Bulgaria. The valley’s pattern of settlement has recently been reconstructed by analysing Byzantine and Slavonic charters from the period 1152 AD until 1395 AD and by proving the existence of altogether 60 settlements. They were all localised and hereafter used to create a model of the abovementioned valley by applying the modified Central Place Theory defined by Koder. In the course of the present case study this “classic” approach of analysis of settlement patterns and its “analogue” picture is evaluated by introducing indices of centrality, namely the Koenig Number (Koenig’s theorem). To push research even further, these results are intertwined with GPS-waypoints and GPS-tracks mapped during surveys in the area of research between 2007 and 2010 and used for a 3D visualisation.

Networks of border zones – multiplex relations of power, religion and economy in South-eastern Europe, 1250-1453 CE
Johannes Preiser-Kapeller
The centuries after the fall of Constantinople to the Crusaders in 1204 were characterized by the political fragmentation of the former imperial sphere of the Byzantine Empire; especially in the period between 1250 and 1453, attempts to establish hegemony by one of the local powers (Byzantium, Bulgaria, Serbia) were followed by phases of disintegration of these polities until the Ottoman State restored “imperial unity” in the region. While political border zones frequently changed, religious denominations (the orthodox Patriarchate of Constantinople, the autocephalous orthodox Churches of Bulgaria and Serbia, the Catholic Church, Islam) tried to preserve or expand their spheres of influence within the entire Balkans; furthermore, local and regional trading networks criss-crossed the region and integrated it in the late medieval “Worldsystem”, which was dominated in the Mediterranean by the cities of Venice and Genoa, which also possessed colonies in the Aegean.

The concepts of network analysis allow us to understand these relations between different communities and authorities in a novel way; Michael Szell, Renaud Lambiotte and Stefan Thurner from the Vienna Complex Systems Research Group argued in a recent paper:

“Human societies can be regarded as large numbers of locally interacting agents, connected by a broad range of social and economic relationships. (…) Each type of relation spans a social network of its own. A systemic understanding of a whole society can only be achieved by understanding these individual networks and how they influence and co-construct each other (…) A society is therefore characterized by the superposition of its constitutive socio-economic networks, all defined on the same set of nodes. This superposition is usually called multiplex, multi-relational or multivariate network.”

We will demonstrate the application of this “multiplexity”-approach for the analysis of various political, religious and mercantile networks which connected individuals and communities from the local and regional level to the level of the competing political, religious and economical centres in the late medieval Balkans within an across border zones. (1) We will present how we obtain relational data from our sources, such as the Register of the Patriarchate of Constantinople, which contains more than 700 documents for the years 1315 to 1402, and the integration of these data into networks of various scales; we will demonstrate how smaller networks can be connected to larger ones and how this influences the characteristics and topologies of networks. Finally, we will illustrate the applicability of this network analytical “toolkit” for other historical disciplines.

Our paper is strongly connected to the study of Mihailo St. Popović, who will present historical-geographical aspects of these phenomena for one specific region.

(1) For a simple example cf. J. PREISER-KAPELLER, Calculating Byzantium? Social Network Analysis and Complexity Sciences as tools for the exploration of medieval social dynamics. Working Paper “Historical Dynamics of Byzantium” 1 (July 2010), http://www.oeaw.ac.at/byzanz/repository/Preiser_WorkingPapers_ Calculating_I.pdf.

Of graphs and graves: towards a critical approach
Ladislav Smejda
This paper describes a project in progress and aims to explore the potential of using graph theory and network analysis for a study of large prehistoric cemetery consisting of 430 graves. The site of Holešov, Czech Republic, dates from the transitional period between the final Stone and early Bronze Ages (around 2000 BC). Although the graph theoretical approaches have recently become very popular in social sciences due to their obvious power to reveal structuring of social networks and physical social space (urbanism and architecture), in archaeology their application still remains rather limited and often restricted to the latter case (i.e. buildings and settlements).

An attempt is made in this presentation to investigate social relationships within a buried population by means of graphs, geographical information systems and other exploratory tools. The work on the introduced case-study however made clear that the critical evaluation of every step in such a procedure is essential. Two main problems were recognized which need to be acknowledged and possibly overcome.

First, in case of human burials we are dealing with symbolically transformed reality, loaded with ideologies and agency. Therefore, what we can learn from the mortuary record is what the prehistoric people wanted to display, what they wanted to stress and underline about their deceased, rather than what relationships living people had among themselves. It is very difficult to understand the meaning of all variants of grave assemblages and possible signs and symbols used in the burial rite when dealing with preliterate societies. Some interesting aspects could nevertheless be placed in social context (age, gender, status?) after mapping relations between graves into networks of several conceptual types and examining their structural properties.

Second, archaeological sources come up as apparently static ‘snapshots’, capable of being mapped and analyzed in geographical space but in fact they are mostly just accumulations of much reduced, fragmented and mixed parts of the past human world over substantial time periods. Having realized this highly variable quality we can ask if there are research questions more appropriate than others for the network analysis. These observations (picked out as typical examples) about the nature of archaeological data raise serious challenges to studies focused on their structuring. They also make a big difference between archaeology and disciplines that can observe objects of their study in living conditions. It certainly must have logical consequences for the application of network analysis in archaeology, and it is a warning for us that unwarranted borrowings of ready-to-use research techniques may produce dubious or misleading results.

References:

Hart, K. 2010: Models of statistical distribution. Anthropological Theory 10/1-2, 67-74.
Šmejda, L. 2010: Time as a Hidden Dimension in Archaeological Information Systems: Spatial Analysis Within and Without the Geographic Framework. Computer Applications to Archaeology 2009 “Making History Interactive”, Online Proceedings .

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Facebooking the Past: a critical social network analysis approach for archaeology
Tom Brughmans
Facebook currently has over 500 million active users, only six years after its launch in 2004. The social networking website’s viral spread and its direct influence on the everyday lives of its users troubles some and intrigues others. It derives its strength in popularity and influence through its ability to provide a digital medium for social relationships.

This paper is not about Facebook at all. Rather, through this analogy the strength of relationships between people becomes apparent most dramatically. Undoubtedly social relationships were as crucial to stimulating human actions in the past as they are in the present. In fact, much of what we do as archaeologists aims at understanding such relationships. But how are they reflected in the material record? And do social network analysis techniques aimed at understanding such relationships help archaeologists understand past social relationships?

This paper explores the assumptions and issues involved in applying a social network perspective in archaeology. It argues that the nature of archaeological data makes its application in archaeology fundamentally different from that in social and behavioural sciences. As a first step to solving the identified issues it will suggest an integrated approach using ego-networks, popular whole-network models, multiple networks and affiliation networks, in an analytical process that goes from method to phenomena and back again.

Keywords: social network analysis, complex systems, social relationships, archaeological data critique, graph theory, archaeological networks

Lines, Damned Lines and Statistics: Unearthing Structure in Ptolemy’s Geographia
Leif Isaksen
Ever since the rediscovery of Ptolemy’s Geographia in 1295, scholars have noted that it is troublingly inconsistent both internally and with the environment in which it was supposedly compiled. The problem for analysts to overcome is that the catalogue has been corrupted, amended and embellished throughout its history. It is therefore imperative to find more robust means to look for structural trends. Recent publications of the theoretical chapters and a digital catalogue of coordinates provide a variety of new possibilities. We are not alone in advocating computational procedures but will discuss two techniques that do not appear to have been considered in the literature so far and the conclusions they appear to give rise to.

First, statistical analysis of the coordinates assigned to localities demonstrates clearly that ostensible precision (whether to the nearest 1/12, 1/6, 1/4, 1/3 or 1/2 degree) varies considerably by region and feature type and is locally heterogeneous. In other words, the composite nature of the data cannot only be confirmed, but we can build a clearer picture of how the sources varied by area. Secondly, while many studies have addressed either the point data or the finished maps, simple linear interpolation between coordinates following the catalogue provides a unique insight into the ‘invisible hand’ of the author(s). The unmistakable stylistic families that emerge, and the occasionally arbitrary limits imposed on them, provide further important evidence about the catalogue’s internal structure.
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