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 🙂

 

JAS

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.

JAS_Brughmans-etal_fig4

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.

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Archaeological and historical network analysts unite!

315px-I_Need_You_on_the_Job_Every_Day_-_NARA_-_534704Network science is becoming more commonly applied in both archaeology and history. But this is not happening without difficulties. Pioneers in both disciplines are now trying to overcome the numerous challenges that still surround their use of network techniques: how to deal with fragmentary data, performing analyses over extremely long time spans, using material data in network science to understand past human behaviour, …. I believe archaeologists and historians should face these challenges together! Through collaboration we might come to a better understanding of the use of network science in our disciplines much faster. In a recently published article in Nouvelles de l’Archéologie, Anna Collar, Fiona Coward, Claire Lemercier and myself show how many of the challenges that archaeologists and historians have identified are actually not discipline-specific: we CAN collaborate to tackle them together. Since this article is in French I wanted to provide an English summary of our argumentation here (written with my co-authors). The full article can be downloaded on Academia or through my bibliography page.

History

One of the key aspects of historical sources, compared to archaeological sources, is that the former often allow for the identification of past individuals, by name, and by role. This richness of data at the individual level means that network analytical methods can be very powerful in the illumination of past social networks and the details of particular places and times – offering, where the data are good enough, a window onto past social lives and interactions, and allowing the synchronic analysis of social networks at a particular moment in time.

However, the issues most commonly mentioned by historical network analysts also concern problematic and incomplete data. These issues are undeniably more significant for archaeology and history than for contemporary social sciences such as sociology. But we should not overestimate their potential impact. Even sociological research in contemporary populations face similar issues where full data may not be available for a variety of reasons, and although the problems are clearly more fundamental in history and archaeology, this also means that researchers in both disciplines have long been accustomed to dealing with, and developing methods at least partially compensating for, partial and biased datasets. As a result, this may be one important area where archaeology and history can contribute its expertise to other disciplines working with imperfect network data.

Archaeology

In contrast to history, archaeology is much less frequently furnished with such focused evidence. In archaeology, individuals are typically identified indirectly through the material remains they leave behind, and even where they can be identified, they often remain without names or specified roles.  Not only is archaeological data typically not ‘individualized’, but it can also rarely be attributed an exact date. Most archaeological data typically has date ranges with differing probabilities attached to them, making the establishment of contemporaneity between entities/potential nodes in networks (e.g. individuals; events; settlements) highly problematic. Because of this, archaeologists have tended to focus on the synchronic study of human behavioural change over the long-term, rather than on the diachronic examination of behaviour and interaction. A further characteristic of archaeological data is that it is also likely to be more strongly geographically grounded. Indeed, the geographical location of archaeological data is often among the few pieces of information archaeologists possess. Finally, network analytical methods in archaeology tend to focus most closely on long-term changes in the everyday lives of past peoples.

Common challenges in archaeology and history

Alongside these differences, there are also a number of common challenges facing archaeology and history, as ultimately both disciplines aim to achieve similar goals relating to understanding past interactions and processes.

The most significant of these common challenges are the fragmentary datasets that often characterize both disciplines; we typically deal with bad samples drawn from populations of unknown size and/or with unknown boundaries, snapshots of the past that are heavily biased by differential preservation and/or observation effects. However we argue that this does not exclude the use network techniques in our disciplines, nor does it limit us to only those research contexts for which high quality datasets are available.

A second issue facing our disciplines is that many methodological and theoretical network approaches have been developed in other disciplines to address particular research themes. As a result, they therefore function according to certain rules and/or have certain specific data requirements that might prevent straightforward applications in our disciplines.

Furthermore,  using a network approach to study a past phenomenon necessarily requires a researcher to make a series of decisions about how the parameters of that phenomenon should be represented – for example, what entities to use as nodes and what forms of relationship to model as vertices. Archaeologists and historians familiar with the analytical and visualization techniques used by researchers studying modern phenomena may find many analytical approaches and visualization techniques that are not appropriate or achievable. The past phenomena we are interested in, the kinds of questions our data allows us to ask, and the often very specific parameters of human behaviour assumed by archaeologists and historians for investigating the past, are likely to mean we will ultimately need to develop purpose-made visualization and analysis techniques. At the least we will need to acquire a critical understanding of the various methods available if we are to represent archaeological and historical network  data in appropriate ways – and indeed, to ‘read’ such visualizations and analysis results correctly.

Finally, the poor chronological control characteristic to a certain extent of historical and to a much greater extent of archaeological datasets, limits our knowledge regarding the order in which nodes and links in networks became salient and also the degree of contemporaneity between nodes. This is likely to have significant ramifications for the ways in which archaeologists and historians visualize and analyse networks, driving a need to consider ‘fuzzy’ networks, margins of error and probabilistic models, as well as the consideration of complex processes of network change and evolution over time.

Unite! Meeting the challenges together

In the recent surge of network applications in archaeology and history, it would seem that the two disciplines have thus far focused their efforts on the more obvious potential applications which mirror those most common in other disciplines, such as the identification and interpretation of ‘small-world’ network structure or the choice of datasets that are readily envisaged as or translated into network data (e.g. road and river networks). Such analyses have demonstrated the potential of the methods for archaeological and historical datasets; however, we believe that potential applications go far beyond this, and that network approaches hold a wealth of untapped potential for the study of the past. To achieve this potential, we will need to become more critical and more creative in our applications, and explore not simply what network science can offer the study of the past, but also what our disciplines offer in terms of developing that science – firstly to tackle specifically archaeological and historical questions, but ultimately to broaden the scope of the science itself as methodologies specifically developed for use in archaeological and historical contexts are taken up for use in tackling similar questions in other disciplines.

TCP (2013_05_12 19_17_14 UTC)Initiatives like The Connected Past and Historical Network Research offer a platform that would allow for exactly this kind of interaction between network scientists and those applying network science to the study of the past. The challenges individual members were encountering in our own research across archaeology and history encouraged us to consider developing a mutually supportive space in which to share concerns and problems, and to discuss ideas and approaches for moving beyond these.

We suggest that simply bringing people together through conferences, workshops, conference sessions and more informal groupings is key to fostering the dialogue between the disciplines that is so important to move forward applications of network analysis to the study of the past. Talking to each other across traditional disciplinary boundaries is vital in the ongoing development of network perspectives on the past. However, as noted above, at the same time we also need to be more sensitive to the specific demands of our disciplinary goals and our datasets and develop new network methods that suit our disciplines better. The sociological roots of most social network analysis software packages means that these are often designed and engineered to address discipline-specific research concepts that may not be appropriate for archaeology and history. SNA software has generally been created to deal with interactions between people in a modern setting – where the individual answers to questions about interactions can be documented with a degree of accuracy. As such, this software and network methodologies in general will need to be applied with care and ideally even developed from scratch for use with networks comprised of nodes which are words, texts, places or artefacts, for the characteristically fragmentary and poorly chronologically controlled datasets of archaeology and history, and for research that aims to go beyond the structuring of individual networks of contemporary nodes to investigate questions of network evolution and change. While interdisciplinary dialogue is crucial, we will need to be sensitive to the discipline-specific idiosyncracies of our data and to critique rather than adopt wholesale practices used in other fields. In this way, rather than apologizing for the ‘deficiencies’ of our datasets in comparison with those characteristic of other disciplines, we will also be able to make novel contributions to the wider field based on the new questions and challenges the study of the past offers network science.

Two months of insanity

Lindroth_The_Absent-minded_ProfessorIt’s finally there: the last two months of my PhD. Ever since I started almost four years ago everyone I talked to with a Dr. in front of their name told me the same thing, that the last few months are the hardest. It sounded as if when you finally decided to finish the damn thing off it starts putting up a fight. This usually finishes in the valiant PhD student winning the battle but loosing part of their sanity and most of their short-term memory in the process. My short-term memory is long gone (this is the main reasons why I claim to show promise for a career as an absentminded academic), but I have held on to my sanity. So far.

As I am working my way through my PhD in the coming two months I will document my struggle and loss of sanity on this blog, hoping it will end in victory. You can expect blog posts about all of the case studies I worked on in the last few years. In particular citation networks and visibility networks. But I will also share some of the conclusions I drew from working with network methods as an archaeologists, the challenges archaeologists are faced with, how we could confront these challenges, and my efforts to make a small contribution towards this. So stay tuned, and above all, please don’t hesitate to comment and provide me with your feedback on my work. I can use it now more than ever! 🙂

On organising the Hestia2 seminar in Southampton

John Goodwin presenting at Hestia2 in Southampton
John Goodwin presenting at Hestia2 in Southampton

As the organiser of the Hestia2 seminar in Southampton I could write about our initial struggle to find a good format, my fight with the university to book a seminar room in a completely booked out campus, discussions with our financial support staff to figure out a balanced budget, the technical flaws with our livestream feed, and of course the many very human feelings like “no-one will turn up!?!” and “what shirt should I wear?”. But none of that would be very interesting to read, and all of these concerns are now firmly pushed to the back of my mind and replaced by the feeling that this seminar was a success!

It will not come as a surprise that the organiser thinks his own event was a success. So let me at least try to come up with some objective-sounding arguments why this was in fact the case.

Multi-disciplinarity: organising a multi-disciplinary event is always risky. You need to address a very diverse target audience and convince them that the topics covered and the discussions will be of interest. People with different backgrounds also tend to talk in different languages: physicists will talk “maths”, classicists will talk “Greek/Roman”, archaeologists will talk “stuff”. The Hestia2 seminar was such a multi-disciplinary event. It was attended by classicists, historians, archaeologists, physicists and designers from the academic, commercial and governmental sectors. Despite this diversity the discussions very often converged into common interests. These included how large datasets like those held by the Ordnance Survey and the Historic Environment Records (HERs) could be usefully combined using new technologies, or how uncertainty about data can be formally expressed and visualised. Finding such common grounds was very much thanks to the chairs of our session. For example, Max Schich confronted the multi-disciplinary background of our audience directly when he asked to what extent individuals need to have skills and knowledge traditionally associated with different disciplines and professions to allow them to apply linked data and network techniques critically and usefully. This question drew very diverse reactions from the audience. Some felt that our educational system should allow for complete diversity and customisations of skills and knowledge, others (and I am part of this particular camp) believe that collaboration is the key, that field specialists should remain specialists but be able to collaborate with specialists in other fields by having some very basic understanding of the other’s “language”, approaches and questions.

Exploration: Hestia2 is not about showing off a great piece of work our team did a few years ago. It’s about learning from different projects’, institutions’ and individuals’ experiences with using innovative technologies to understanding conceptions of space. It’s about exploring the potential of such techniques for providing innovative insights into old datasets, or for allowing us to ask new questions of our data. The Southampton seminar definitely had that exploratory vibe. Very different techniques and projects were presented. The first three talks very much set the scene by giving an overview of different approaches. Max Schich introduced us to networks, Alex Godden provided an insight into the issues surrounding the aggregation and management of historical/archaeological data, and John Goodwin showed how the Ordnance Survey (OS) is implementing linked data. The discussions that followed showed a genuine interest in innovative approaches but also a constant concern with getting at the fundamental issues that keep all this innovation together. For example, in our discussion we never restricted ourselves to asking how something could be done, but always focused on why we should do it in the first place. The question of why HER data could not be seamlessly linked with OS data, for example, was not because of technological restrictions but concerns about protecting cultural heritage and also commercial concerns. Once such concerns were addressed we turned our attention to how combining such diverse datasets could allow us to ask new research questions, or could lead to a better management of historical resources.

Weather: it has sunny and hot. That makes every event an instant success!

I am very much looking forward to the second Hestia2 seminar in Stanford, where I will be able to put my feet up a little and enjoy another round of stimulating multi-disciplinary exploration.

Connected Past workshop registration open

TCPWorkshop Announcement:

THE CONNECTED PAST: NETWORK ANALYSIS FOR ARCHAEOLOGISTS AND HISTORIANS

Networks offer one of the newest and most exciting approaches to archaeological and historical data analysis, and over the last two years, the The Connected Past team has brought together scholars from across the globe to discuss their research, with a session at Birmingham TAG 2011, the Southampton conference in March 2012, a session at the SAAs in Hawaii in April this year, and a collaboration with HESTIA this coming July.

But we’re also aware that starting to do network analysis isn’t always easy. It can be difficult to know which software to use, how to present data, what questions to ask, and what results really show. Because it’s hard for researchers at all levels who are starting to think about network analysis, we are delighted to announce that we have put together a programme for a two-day practical workshop at the University of Southampton on 17-18 September 2013.

The cost of the workshop is £20. PLACES ARE LIMITED TO 20. To register your interest, please email connectedpast@soton.ac.uk with a short statement detailing why you want to participate. We will be in touch once the registration deadline (22nd July) has passed. The programme can be found below and on The Connected Past website.

In addition, for those who want to overdose on networks, Southampton will also be hosting the 12th Mathematics of Networks meeting on 16th September. It’s very multi-disciplinary, with a focus on social science applications and the technical side of things.

Programme:

Tuesday 17th September

Morning:
• Introduction to networks in archaeology and history
• Preparing data for network analysis
• network creation and visualisation
Lunch
• Archaeological and historical case studies
• Round table discussion
Reception at the Institute for Complex Systems Simulation

Wednesday 18th September

Morning:
• Network analysis software
• Analysing network structure
Lunch
• What method to use?
• Geographical network techniques
• Issues in archaeological and historical network analysis

Tutors:
Andy Bevan (UCL)
Tom Brughmans (Southampton)
Anna Collar (McDonald Institute, Cambridge)
Fiona Coward (Bournemouth)
Marten Düring (Nijmegen)
Claire Lemercier (Sciences-Po, Paris)
Angus Mol (Leiden)

Introducing ‘A Connected Island?’: how the Iron Curtain affected Archaeologists

Eötvös Loránd University (University of Budapest)
Eötvös Loránd University (University of Budapest)
After the Second World War the Iron Curtain sliced through the very centre of Europe forming a very real divide in both political and daily lives. In the second half of the 20th century the Soviet regime introduced a new structure to the academic institutions to countries like Poland, Hungary and former Czechoslovakia, including restrictions on contacts with the Western world and ideological pressure previously unknown in these parts of Europe. How did this situation affect researchers on both sides? Was Central European Academia really isolated from western influences? A new project funded by SotonDH aims to address this issue using Palaeolithic archaeology as a case study. In ‘A connected island? Evaluating influence and isolation of Central European Palaeolithic researchers during communism’ ACRG members Iza Romanowska and Tom Brughmans combine a traditional historiography with novel citation network analysis techniques to approach this issue from a new angle.

Isolated or not?

A heated debate has been taking place in Central European archaeology in the last two decades regarding the issue of isolation (or the lack thereof) from western influences during the second half of the 20th century. Difficulties related to obtaining the necessary passports and visas, the disparity in the values of currencies, and only limited formal international links between research institutions restricted research visits, data collection, literature review, and conference attendance. Equally hindering was the limited circulation of Western archaeological journals within the Soviet Bloc countries, and restricted accessibility to archaeological publications in general. This could have been further aggravated by language barriers and, to some extent, different disciplinary interests. All this does not necessarily mean that Central European researchers were completely unaware of what was happening in the West, as if living on an island unconnected to the rest of the world and immune to external influences.

It is difficult to quantitatively determine to what degree these limitations affected Central European researchers. The project team argues that citation data might allow (at least in part) for such a quantitative evaluation. When a researcher cites the work of another scholar they express in a very formalised way that they were influenced by this person. Citations are like handy proxies for tracing lines of knowledge dissemination and academic influence, obviously not fully representative for these very complex processes, but well suited to quantify the ‘awareness’ of other peoples research.

‘A connected island?’ will collect and explore citation data for Central European Stone Age studies, a relatively small but highly international research field that forms a well-defined case-study suitable for quantitative analysis. The project will initially focus on the Lower and Middle Palaeolithic of Poland, former Czechoslovakia and Hungary. The citation behaviour of scholars working in these countries will be confronted with that of Western European Palaeolithic researchers. The proposed project therefore aims to explore the degree of interaction and academic influence between Central and Western European researchers in Lower and Middle Palaeolithic archaeology during communism (1945-1989) through citation network analysis, in order to evaluate the hypothesis that Central European researchers worked in strong academic isolation.

Data collection in Hungary

Digital citation datasets are available online through services like Google Scholar or Web of Knowledge. However, neither of these is very comprehensive for books or local non-peer-reviewed journals where a lot of the Palaeolithic archaeology of Central Europe was published in. So despite of the revolution in digital data collection brought about by the World Wide Web, a critical analysis could not be performed without visiting key libraries and research institutions in Central Europe. So the ‘connected island’ team hit the road.

The first phase of data collection was conducted in May 2012. In this first round a visit was paid to the Institute of Archaeology at the Jagiellonian University in Krakow. Thanks to a bursary from SotonDH the team could perform the second phase of data collection at the Hungarian Academy of Sciences and the Institute of Archaeological Science at the Eötvos Loránd University, both in Budapest. The third phase will aim to collect relevant literature from the Ústav pro pravěk a ranou dobu dějinnou at Charles University in Prague.

A few preliminary results

The widely held assumption that archaeological data from Central Europe was published in local languages is false. At least half, if not more, of Central European archaeology publications from this period were published in either German, French or English alongside the national language. The image that all countries under the influence of the former Soviet Union published in Russian is incorrect.

Just like their Western European or American colleagues Central European Palaeolithic researchers worked within a very strong ‘Bordean’ framework (named after a famous French researcher: Francois Bordes and his wife Denisse de Sonneville-Bordes).

From a cursory check, one gets an impression that Palaeolithic researchers in Central Europe were well informed of the developments on the other side of the Iron Curtain and quoted western authors extensively. The same can be said in reverse for their Western European colleagues who occasionally quote one or two Central European sites but did not seem to be aware of the full scope of the research happening in the region.

A second blog post on this project will follow soon featuring the first results of the citation network analysis, aimed at exploring this notion of unbalanced citation behaviour between the Eastern and Western researchers. Watch this space.

Iza Romanowska and Tom Brughmans

An overview of The Connected Past

Over the weekend of 24-25 March 2012 a group of 150 archaeologists, historians, mathematicians, computer scientists, physicists and others from 19 different countries met at The University of Southampton. Their objective: to discuss the critical application of network and complexity perspectives to archaeology and history. The result: a stimulating and friendly gathering of academics from very diverse backgrounds who collectively created the exciting discussion platform the organisers believe is crucial to the development of future critical applications in our disciplines.

The last few weeks were hectic for Anna Collar, Fiona Coward and myself. There were many last-minute decisions to be made and problems to be solved. But in the end everything and everyone arrived on time to kick-start the symposium. Most delegates arrived from all over Europe and North America, and some joined us from as far as Australia and Japan. We were happy to welcome delegates from over 60 different universities. The most important work during the symposium took place behind the scenes by Lucie Bolton and her great team of volunteers who were there to welcome all delegates at 8am and make sure they were fuelled with lunch, coffee and cakes throughout the day. The Connected Past would not have been possible without them.

Jon Adams, head of the Department of Archaeology here in Southampton, opened the symposium and introduced our first keynote speaker Alex Bentley. Alex discussed in what cases certain types of network approaches are useful when exploring complex social systems. His paper provided a great start of the conference by setting out a framework for complex systems simulation and identifying the role networks could play within this. A first session of the symposium followed with a very diverse group of papers discussing a range of theoretical and methodological issues. Tom Brughmans explored the evolution of formal archaeological network analysis through a citation network analysis. Johannes Preiser-Kapeller argued for the incorporation of Luhmann’s systems theory in historical network approaches. Andy Bevan explored the issues involved in tracing ancient networks in geographical space. After a coffee break Astrid Van Oyen presented us with the Actor-Network-Theory perspective and how this might be usefully applied in an archaeological context. Søren Sindbæk made some very critical remarks concerning a direct mapping of exchange networks from distributions of archaeological data. Finally, Marten Düring presented a particularly fascinating approach of support networks for persecuted Jews in World War II and compared the usefulness of different centrality measures on it.

After lunch we reconvened for a session called ‘Big data and archaeology’, which included presentations of big datasets that showed particular potential to explore using networks on the one hand and archaeological applications of network analysis on the other. The session was opened by Barbara Mills who presented the work of her team on exploring distribution networks of a large archaeological dataset from the US southwest. Caroline Waerzeggers presented a dataset of tens of thousands of cuneiform tablets which hold a large variety of past relationships that can be usefully explore with network techniques. Mark Depauw and Bart Van Beek similarly presented an impressive dataset which includes references to almost half a million people living in Graeco-Roman Egypt. After tea Eivind Heldaas Seland introduced us to a highly qualified view of networks of travel and religion in late antiquity. Alessandro Quercia and Lin Foxhall presented their networks of loom weights, which is part of the wider Tracing Networks project. Angus Mol took us to the Caribbean with his network approach of a rather small but fascinating lithic assemblage. Finally, Craig Alexander discussed his study of visibility networks in Iron Age Valcamonica.

At the end of the day we had the pleasure of listening to Carl Knappett live from Toronto via a Skype call. We decided to go for this low-tech option because sadly we could not guarantee tech-support during the weekend and wanted to avoid complications. I am sure this is the first time Carl had a Skype meeting with 150 people at the same time. Carl Knappett suggested that in order for network approaches to be usefully applied in archaeology we need be aware of the diversity of available approaches and preferably work in collaboration with network specialists. In some cases, however, networks are not the best perspective to approach our archaeological questions. In his recently published ‘An archaeology of interaction’ Carl points to a wide range of theories and methods that may or may not work within the same framework, but knowledge of this diversity might lead to their more critical and useful applications. This second keynote presentation was followed by a wine reception and a visit to our local pub The Crown.

After a long night out and a nights-sleep further shortened by daylight savings time we were surprised to see almost all delegates appear at 9am to listen to our third keynote Irad Malkin. Irad recently published ‘A small Greek world’ in which he sees the emergence of Greek identity through network goggles by using a vocabulary adopted from complex network analysis to describe the processes he identified in ancient sources. Irad’s keynote address stressed how a networks approach allows us to revisit old questions and how it allows for spatial structure to be compared with other types of relationships. The subsequent session titled ‘Dynamic networks and modelling’ began with a great presentation by Ray Rivers stressing that archaeologists need to be aware of the implications of decisions made when modelling the past and selecting ‘Goldilocks’ networks that seem just right. Next, Anne Kandler presented her network model for exploring the transmission of ideas, which shows how the structure of complex networks influences cultural change. Caitlin Buck presented the work by her team on a new (and very robust looking) model for the spread of agriculture in Britain and Europe at large. After the break Tim Evans presented a much needed paper comparing different network models and their potential uses. The discussions after this paper revealed that such a comparison along with archaeological case studies would be a very welcome resource to archaeologists interested in networks. Juan Barceló presented a Bayesian network approach to explore causal factors determining the emergence and the effects of restricted cooperation among hunter-gatherer societies. Marco Büchler presented his fascinating work on text re-use graphs he and his team in of the eTraces project in the Leipzig centre for eHumanities are working on.

After lunch we had the pleasure of listening to papers in our last session ‘Personal, political and migration networks’. Wilko Schroeter presented on marriage networks of Europe’s ruling families from 1600-1900. Ekaterini Mitsiou moved our attention to the Eastern Mediterranean in her discussion of aristocratic networks in the 13th century. Evi Gorogianni made us look at dowry in a new way by stressing the relationships they establish and express. After tea Elena Isayev made us explore the early 3rd century BC networks of Italy outside the Italian peninsula. Claire Lemercier provided us with some critical comments on the historical use of formal network techniques and illustrated this through a case study on migration in northern France. Amara Thornton traced networks of individuals linked to the British School of Archaeology in Jerusalem. Finally, Katherine Larson showed us a particularly creative way of seeing networks in the archaeological record by linking sculptors’ signatures on ancient statues.

In our eyes The Connected Past was a great success. We enjoyed the experience of organising the event and were delighted with the overwhelming response to our call for papers and registration. We received some great reviews from Tim Evans and Matteo Romanello. In the end, however, it was the delegates themselves who seized the opportunity to engage in multi-disciplinary discussions and to consider future collaborations in innovative research directions.

The Connected Past does not end here! In some time we will make some of the recorded talks available online, we will publish the proceedings and we have plans for future meetings. All to be revealed in time. For now all we want to say is: thank you for a fascinating weekend and keep up the multi-disciplinary discussions!

CAA2011 networks session summary and discussion

The networks session at CAA2011 in Beijing was a success! We had some great papers and a fascinating discussion. Read the summaries of the papers, the questions and answers, as well as the discussion here. Read more about the session, including the abstracts and the introduction on the dedicated page.

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 200 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 470 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 470 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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