Registration open The Connected Past @ Imperial College London

July 23, 2014

TCP (2013_05_12 19_17_14 UTC)The programme has been finalised and it’s looking like it will be a great event. Time to plan your trip to London and register for The Connected Past conference and workshop at Imperial College London, 8-9 September 2014!

Register here for this event.


The Connected Past: archaeological challenges and complexity is a one and a half day multi-disciplinary meeting to explore how concepts and techniques from network- and complexity science can be used to study archaeological data. These challenges include the use of material data as proxy evidence for past human behaviour, questions about long-term processes of social change, and the fragmentary nature of archaeological data. We aim to bring together physical scientists and archaeologists in order to highlight the challenges posed by archaeological data and research questions, and explore collaborative ways of tackling them using perspectives drawn from network and complexity science.

The workshop is funded in part by an EPSRC NetworkPlus grant addressing one of Physics Grand Challenges: Emergence and Physics Far From Equilibrium so this meeting will explore ways that archaeological problems can be tackled from such a viewpoint. There is likely to be a small registration fee to cover local costs but some of the funding can and will be used to support travel by some UK based researchers.

The meeting will take place on the afternoon of Monday 8th September and all day Tuesday 9th September at Imperial College London. A hands-on introductory workshop is planned for the morning of Monday 8th September.

Keynote talks. The meeting will feature keynote talks by Alan Wilson, University College London, and Ulrik Brandes, University Konstanz (a further additional keynote will be announced soon). Shorter talks will be given by other invited speakers and from researchers submitting abstracts. Finally, at a later date we will issue a call for some quick fire (five minute) talks to allow researchers at all stages of their career to participate.

Further Information. Full details are available on the web site at

On Twitter follow the hashtag #tcp2014

Huxley 139 Lecture Theatre
More details on how to get to the meeting can be found here.

Organisers: Tim Evans (chair), Ray Rivers, Tom Brughmans, Anna Collar, Fiona Coward.
Advisory Committee: The Connected Past committee


To be announced very soon.


The registration is £40 for those with financial support or waged, £20 for students and others with limited or no financial support. This will help us cover the local expenses for your attendance, other costs (such as keynote speakers) will be supported by the EPSRC networkplus grant. Registration provides lunch on the Tuesday, coffee/tea breaks plus the informal social event on the Monday evening. Except for speakers, please REGISTER HERE for TCP2014. Speakers should register using the link provided in a forthcoming email. If you have any questions about registration, please get in touch. Also note there are a few travel bursaries, see below.

Travel Bursaries

Some support is available to cover travel and other costs of UK based researchers attending the meeting. If you wish to be considered for such support, please send a request explaining why you should be considered for a bursary to the same address as for papers with the subject “Bursary application [your name]” to Bursaries will be given out from mid June onwards while funds remain.

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

July 17, 2014

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 🙂



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.


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.

Archaeological and historical network analysts unite!

July 10, 2014

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.


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.


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.