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.

Complex networks at SIDEER symposium

February 24, 2014

sideerNetwork science is a fiercly multi-disciplinary undertaking these days. This is again confirmed by the SIDEER symposium on ‘Exploring real world networks: from genes to ecosystems’. The programme lists a few well-known names in network science (including Shlomo Havlin who co-developed the interesting ‘networks of networks’ concept for understanding the inter-dependence of networks), and it also includes a few humanities topics. No history or archaeology as far as I know. Still, this event might be of interest to some readers of this blog.

When? 11-13 March 2014
Where? Ben Gurion University of the Negev, Midreshet Ben Gurion, Israel
More info online.

The complexity of real world systems, from the molecular level of gene complexes all the way up to large scale dynamics of ecosystems and social structures, can be captured by network analysis. The emerging discipline of complex network analysis studies the topologies of the networks of complex interactions amongst many participants of a given system. By doing so, insights and patterns, undetected at the individual level, can be revealed and analyzed at the network level. The Sixth Annual SIDEER Symposium will focus on the latest theory and methods of network analysis aimed at studying networks found in the real world, as well as interesting implementation of network analysis in varied scientific fields such as gene networks, social networks in humans and animals, ecological-epidemiological networks, climatic networks and many more.

Registration AHCN 2013 open

May 17, 2013

Screen shot 2013-02-19 at 10.16.18Three years ago I attended the Arts, Humanities, and Complex Networks satellite at NetSci. It was a great event, really multi-disciplinary. Registration is now open for the 2013 edition. It is free but tends to fill up quickly, so reserve your seat soon. The line-up looks great.

Dear all,


Arts, Humanities, and Complex Networks
– 4th Leonardo satellite symposium at NetSci2013

on Tuesday, June 4, 2013 at DTU Copenhagen, Denmark.

featuring keynotes by Denny Vrandečić (Wikimedia Foundation, Germany), Paolo Ciuccarelli (DensityDesign, Italy), Scot Gresham-Lancaster (The Hub, USA), and contributions by Doron Goldfarb et al. (Austria), Emoke-Agnes Horvat et al. (Germany), Marnix van Berchum (The Netherlands), Bruno Mesz (Argentina), Santiago Ortiz (Colombia), Ruth Ahnert (UK), Thomas Lombardi (USA), and François-Joseph Lapointe (Canada). We had a new record acceptance rate of 14.5%.

Attending the symposium is free of charge, but requires registration. Tickets are given out in a first come, first serve basis, to both NetSci2013 main conference attendees as well as external guests. Please be aware that registration MAY FILL UP FAST. Please also note that we partner with an associated evening event below.

FOR THE FULL PROGRAMM and more information on our symposium, including the Book of Abstracts and an introductory video, please go to

Right after our symposium at 19:00, Leonardo/OLATS and the Copenhagen Medical Museion partner to present László Barabási, François-Joseph Lapointe, Annamaria Carusi, and Jamie Allen to discuss “The Data Body on the Dissection Table”. Refreshments will be provided. Please register separately at

PLEASE ALSO CHECK OUT OUR COMPANION WEBSITE with a collection of past abstracts, videos, links to our ongoing Special Section in Leonardo Journal, and our evolving eBook at MIT-Press at


Enthusiastic and curious to see you in Copenhagen,

The Arts, Humanities, and Complex Networks organizers,
Maximilian Schich, Roger Malina, Isabel Meirelles, and Annick Bureaud

CFP: Arts, Humanities, and Complex Networks

February 25, 2013

Screen shot 2013-02-19 at 10.16.18I have advertised the Arts, Humanities, and Complex Networks symposia a few times before and have attended one of them (presentation, paper). It proved a really fascinating multi-disciplinary event where I learned a lot and met many like-minded people. So for all of us doing networks in Arts and Humanities, come down to Denmark and present at the Symposium.

Deadline call for papers: 31 March 2013
For more info and submission go to the symposium website.

We are delighted to invite submissions for

Arts, Humanities, and Complex Networks
— 4th Leonardo satellite symposium at NetSci2013

taking place in Copenhagen at DTU – Technical University of Denmark,
on Tuesday, June 4, 2013.

For submission instructions please go to:

Deadline for submission: March 31, 2013.
Notifications of acceptance will be sent out by April 8, 2013.

The overall mission of the symposium is to bring together pioneer work in the overlap of arts, humanities, network research, data science, and information design. The 2013 symposium will leverage interaction between those areas by means of keynotes, a number of contributions, and a high-profile panel discussion.

In our call, we are looking for a diversity of research contributions revolving around networks in culture, networks in art, networks in the humanities, art about networks, and research in network visualization. Focusing on these five pillars that have crystallized out of our previous meetings, the 2013 symposium strives to make further impact in the arts, humanities, and natural sciences.

Running parallel to the NetSci2013 conference, the symposium provides a unique opportunity to mingle with leading researchers in complex network science, potentially sparking fruitful collaborations.

As in previous years, selected papers will be published in print, both in a Special Section of Leonardo Journal MIT-Press and in a dedicated Leonardo eBook MIT-Press.

Best regards,
The AHCN2013 organizers,
Maximilian Schich*, Roger Malina**, and Isabel Meirelles***

* Associate Professor, ATEC, The University of Texas at Dallas, USA
** Executive Editor at Leonardo Publications, France/USA
*** Associate Professor, Dept. of Art + Design, Northeastern University, USA

Basketball is a network

December 14, 2012

Screen shot 2012-12-10 at 10.47.22Ever thought of basketball players as nodes in a small network, connected by passes? A recently published study did just that, revealing that the aggregated decisions on a basketball court reflect strengths and weaknesses in a team’s strategy. A team led by Jennifer Fewell and Dieter Armbruster published their findings in the journal PLoS One. They tracked all passes between basketball team members during the 2010 NBA play-offs. The networks reveal differences between teams’ strategies, and centrality and entropy measures are used to capture these differences.

Wired magazine wrote a very readable overview of this and similar sports statistics work.

Since the article is published in an open access journal it is freely available to all, isn’t that great. Here is the article abstract:

We asked how team dynamics can be captured in relation to function by considering games in the first round of the NBA 2010 play-offs as networks. Defining players as nodes and ball movements as links, we analyzed the network properties of degree centrality, clustering, entropy and flow centrality across teams and positions, to characterize the game from a network perspective and to determine whether we can assess differences in team offensive strategy by their network properties. The compiled network structure across teams reflected a fundamental attribute of basketball strategy. They primarily showed a centralized ball distribution pattern with the point guard in a leadership role. However, individual play-off teams showed variation in their relative involvement of other players/positions in ball distribution, reflected quantitatively by differences in clustering and degree centrality. We also characterized two potential alternate offensive strategies by associated variation in network structure: (1) whether teams consistently moved the ball towards their shooting specialists, measured as “uphill/downhill” flux, and (2) whether they distributed the ball in a way that reduced predictability, measured as team entropy. These network metrics quantified different aspects of team strategy, with no single metric wholly predictive of success. However, in the context of the 2010 play-offs, the values of clustering (connectedness across players) and network entropy (unpredictability of ball movement) had the most consistent association with team advancement. Our analyses demonstrate the utility of network approaches in quantifying team strategy and show that testable hypotheses can be evaluated using this approach. These analyses also highlight the richness of basketball networks as a dataset for exploring the relationships between network structure and dynamics with team organization and effectiveness.

CFP CAA 2013 Perth and our Complexity sessions

August 15, 2012

The CAA (computer applications and quantitative methods in archaeology) is one of those conferences I actually look forward to each year. It has a big community of genuinely great people, it’s always a good experience. Next year it will be held in Perth, Australia. The call for papers is just out, and it looks like there will be quite a few interesting sessions on quite diverse topics. Download the CFP and list of sessions here.

I am involved in one session and one workshop this year, both with Iza Romanowska, Carolin Vegvari and Eugene Ch’ng. Our papers session is entitled “S9. Complex systems simulation in archaeology” and our hands-on workshop “W1. Complex Systems and Agent-Based Modelling in Archaeology”. Feel free to submit an abstract to the session. We hope we will spark some interest in complexity and good discussion with both the session and workshop.

Here are the abstracts:

S9. Complex systems simulation in archaeology. Chairs: I. Romanowska, T. Brughmans. Discussants: E. Ch’ng, C. Vegvari Format: Paper presentation (LP)

A complex system is “a system in which large networks of components with no central control and simple rules of operation give rise to complex collective behaviour, sophisticated information processing, and adaptation via learning or evolution.” Mitchell 2009: 14. Complexity has been proclaimed as a new paradigm shift in science almost half a century ago. It developed as a response to the reductionist approach of René Descartes and the idea of a ‘clockwork universe’ that dominated past thinking for many centuries. Complexity brings a fresh alternative to this mechanistic approach. Complex Systems exist in every hierarchy of our world, from the molecular, to individual organisms, and from community to the global environment. This is why researchers in many disciplines, including archaeology, found particularly appealing the idea that global patterns can emerge in the absence of central control through interaction between local elements governed by simple rules (Kohler 2012). As a result, the unifying phrase ‘the whole is greater than the sum of its parts’ (Aristotle, Metaphysica 10f-1045a) became the common ground for scholars in many disciplines.
Due to the complex nature of interactions, the study of complex systems requires computational tools such as equation-based modelling, agent-based modelling (ABM) and complex network analysis. In recent years the number of archaeological applications of complex systems simulation has increased significantly, not in the least due to a wider availability of computing power and user-friendly software alternatives. The real strength of these tools lies in their ability to explore hypothetical processes that give rise to archaeologically attested structures. They require archaeological assumptions to be made explicit and very often force researchers to present them in quantifiable form. For example, vague concepts such as ‘social coherence’, ‘connectivity’ or even seemingly explicit ‘dispersal rates’, often have to be given numeric values if they are to be integrated into computational models. Computational tools also allow for testing alternative hypotheses by creating ‘virtual labs’ in which archaeologists can test and eliminate models which, although superficially logical, are not plausible.
The main contribution that complexity science perspectives have to offer archaeology is the wide set of modelling and analytical approaches which recognise the actions of individual agents who collectively and continually create new cultural properties. Indeed, it has been argued that a complexity science perspective incorporates the advantages of culture historical, processual and post-processual paradigms in archaeology (Bentley and Maschner 2003; Bintliff 2008). Quantifiable complex systems simulations and mathematical modelling can provide a way to bridge the gap between the reductionist approach and the constructionist study of the related whole (Bentley and Maschner 2003).
This session aims to reflect upon and build on the recent surge of complex systems simulation applications in archaeology. Innovative and critical applications in analytical modelling, ABM, network analysis and other methods performed in a complexity science approach are welcomed. We hope this session will spark creative and insightful discussion on the potential and limitations of complexity science, possible applications, tools as well as its theoretical implications.

W1. Complex Systems and Agent-Based Modelling in Archaeology. Chairs: E. Ch’ng, C. Vegvari. Discussants: I. Romanowska, T. Brughmans

Modelling in various forms has always been an integral part of archaeology. In the broadest sense, archaeology is the study of human activities in the past, and a model is a simplified representation of reality. As a map is a useful abstract of the physical world that allows us to see aspects of the world we chose to, so a computational model distils reality into a few key features, leaving out unnecessary details so as to let us see connections. Human societies in their environmental context can be considered as complex systems. Complex systems are systems with many interacting parts, they are found in every hierarchy of the universe, from the molecular level to large planetary systems within which life and humanity with its cultural developments occur. Formal modelling can help archaeologists to identify the relationships between elements within a complex socio-environmental system in that particular hierarchy. Simulating large populations and non-linear interactions are computationally expensive. In recent years, however, the introduction of new mathematical techniques, rapid advances in computation, and modelling tools has greatly enhanced the potential of complex systems analysis in archaeology. Agent-Based Modelling (ABM) is one of these new methods and has become highly popular with archaeologists. In Agent-Based Modelling, human individuals in ancient societies are modelled as individual agents. The interaction of agents with each other and with their environment can give rise to emergent properties and self-organisation at the macro level – the distribution of wealth within a society, the forming of cohesive groups, population movements in climate change, the development of culture, and the evolution of landscape use are among the examples. Thus, the application of Agent-Based Models to hypothesis testing in archaeology becomes part of the question. The ability to construct various models and run hundreds of simulation in order to see the general developmental trend can provide us with new knowledge impossible in traditional approaches. Another advantage of agent-based models over other mathematical methods is that they can easily model, or capture heterogeneity within these systems, such as the different characteristics (personalities, gender, age, size, etc), preferences (coastal, in-land, food, fashion), and dynamics (microstates of position and orientation).
We would like to invite archaeologists new to complex systems and Agent-Based Modelling for an introductory workshop on Complex Systems and Agent-Based Modelling in archaeology. The workshop introduces the concept of Complexity in archaeology, drawing relationships between Information, Computation and Complexity. The practicality of the workshop leads beginners in building simple agent- based models and provides a means to build more complex simulations after. Participants knowledgeable in Complexity wishing to gain insights on real-world applications of Complexity will benefit from this workshop. Participants will get the opportunity to experiment with simple models and draw conclusions from analysis of simulations of those models. Programming experience is not required as the workshop leads beginners from the ground up in modelling tools.

Video: urban network analysis

July 16, 2012

This video is an impressive ad for the Urban Network Analysis toolkit, which I have never worked with by the way. Network analysis in urban environments is quite popular since it is relatively straightforward to identify the obvious nodes and links. A simple transport network can consist of streets as links connecting nodes at the crossing of these links. Urban Network Analysis seems to add buildings and a large variety of attributes (like jobs, residents, …). It uses this to create network maps of cities that can integrated with ArcGIS10 and analysed using network analysis measures. The measures illustrated in the video are quite simple and common, and by no means exclusive to urban network analysis. But they do become quite powerful when looking at large networks, like entire cities for example. The approach taken here has much in common with Space Syntax, although without the theoretical/interpretative baggage. The video is a pretty good introduction to how to see networks in an urban environment, so do have a look.

Urban Network Analysis Toolbox Introduction from Tolm on Vimeo.