To be Published as
Brughmans, T. (Forthcoming). Facebooking the past: a critical social network analysis approach for archaeology. In Chrysanthi, A., Flores, M. P., Papadopoulos, C. (eds), Thinking beyond the Tool: Archaeological Computing and the Interpretative Process. Oxford, Archaeopress - British Archaeological Reports.
Facebooking the Past: a critical social network analysis approach for archaeology
Tom Brughmans
Abstract
Facebook currently has over 500 million active users, only six years after its launch in 2004. The
social networking website's viral spread and its direct influence on the everyday lives of its users
troubles some and intrigues others. It derives its strength in popularity and influence through its
ability to provide a digital medium for social relationships.
This paper is not about Facebook at all. Rather, through this analogy the strength of relationships
between people becomes apparent most dramatically. Undoubtedly social relationships were as
crucial to stimulating human actions in the past as they are in the present. In fact, much of what we
do as archaeologists aims at understanding such relationships. But how are they reflected in the
material record? And do social network analysis techniques aimed at understanding such
relationships help archaeologists understand past social relationships?
This paper explores the assumptions and issues involved in applying a social network perspective in
archaeology. It argues that the nature of archaeological data makes its application in archaeology
fundamentally different from that in social and behavioural sciences. As a first step to solving the
identified issues it will suggest an integrated approach using ego-networks, popular whole-network
models, multiple networks and affiliation networks, in an analytical process that goes from method
to phenomena and back again.
Keywords: social network analysis, complex systems, social relationships, archaeological data
critique, graph theory, archaeological networks
Introduction
Does social network analysis allow archaeologists to understand past social relationships? The
social network perspective is based on the assumption that relationships between individuals shape
their actions, and it offers a set of theories and techniques for understanding human behaviour
through relationships between individuals or communities and their affiliations. But can this
perspective just be adopted from the social and behavioural sciences by archaeologists and be
applied to archaeological data? Does it succeed in explaining the full complexity of past social
relationships? This paper aims at surfacing fundamental issues with the archaeological application
of social network analysis which have been largely ignored in previous applications. A number of
suggestions will be made as a first step to overcoming these issues.
This paper purposefully only covers social network analysis. A general discussion of the
archaeological potential of other network-based approaches has been published recently
(Brughmans 2010).
Once upon a time … A short fiction about a network and a politician1
Once upon a time in Rome there was a man Called Cicero. He was a great orator and one of the best
lawyers in the city. In fact, only one other lawyer was said to be his superior, a man called
Hortensius. As Cicero had achieved almost all he could within the boundaries of his profession, he
decided to take up politics. It was his lifelong dream to become a Consul of Rome, so Cicero rose to
the challenge and signed himself up as a candidate for the coming elections. Consular elections in
Rome, as you may know, worked according to a very familiar political principle: the most popular
individual gets the job. Popularity, however, is not exempt from another and possibly even more
familiar human principle: everything has a price. In Rome votes could be bought, so in the end the
richest person would be elected.
Our friend Cicero might not have been counted among the wealthiest men in Rome and neither was
1 This short fiction is very loosely based on Robert Harris' novel Imperium (2006). It is adapted by the author and does not aim at being historically accurate in any way.
he a member of the established political families. There was no doubt in his mind that the elections
would be the most demanding struggle in his life so far. He did have one advantage however, Cicero
was quite a popular man. In fact, according to his Facebook profile (Figure 1) he had over one
thousand friends. Everyone even remotely familiar with Facebook, or indeed the idea of friendship,
will know that that is a large number of friends to have. Among those friends were a couple of very
influential and popular men like Atticus, who frequented in many different social circles including
the highest echelons of Roman aristocracy. Cicero himself was mostly popular with the rural elite as
well as with some groups of the Roman plebs.
Cicero knew, however, that one thousand friends would not be enough to ensure victory in the
coming elections. If he was to have a real chance at actually becoming the next consul of Rome,
Cicero would need to distinguish himself in some way from the other candidates. To do this, Cicero
did not invest too much effort in the electorate of Rome, with its established political affiliations
and corruption scandals that would even disgust the most inhumane of persian kings. Instead he
turned his attention to the communities living to the north of the city, who could cast their vote in
this year's election for the very first time. By browsing through some public Facebook profiles
Cicero found out that these new voters were very different from the Romans, as they shared
different Facebook pages and visited each other's farms in Farmville and so on. Cicero did not have
any friends from these communities himself, however, so he asked some of his Facebook friends
who lived closer to them to find out who among the new voters were the most popular and
influential people. Cicero visited these people personally and added them as friends on Facebook.
By doing this Cicero became part of a totally new and isolated network within the electorate. He
explored who was friends with who and what topics they liked to discuss. Armed with this
knowledge Cicero gave public talks in some of the most popular meeting places in the north about
the issues his new Facebook friends cared about. Every day more and more northerners added
Cicero as a friend on Facebook, so that their support in the coming elections was as good as
guaranteed.
Cicero decided to spend the last few weeks before the elections in Rome, so his supporters there
would not have the feeling he abandoned them. But on his way back from the north he received a
disturbing e-mail. Antonius Hybrida apparently removed Cicero as a friend on Facebook. This was
a real blow for Cicero, because Hybrida was one of the only aristocrats that publicly supported
Cicero. Indeed, not too long ago our friend successfully defended Hybrida in court when he was
accused for his inappropriate lifestyle. Cicero knew all too well that the man was an alcoholic and a
brute, but an aristocratic alcoholic and a popular brute would still be a valuable ally for Cicero in
the elections. Now Cicero was puzzled why he lost the little support he had from the aristocrats. He
decided to send his secretary Tiro to Rome ahead of him to find out why he was betrayed by
Hybrida. And sure enough the reliable Tiro presented him with valuable information when Cicero
arrived in Rome. Apparently four rich and mighty aristocrats, Crassus, Caesar, Catilina and
Hybrida, had their eyes on becoming the most powerful men in the empire. Catilina and Hybrida
would be their candidates for the consulate and the four of them conspired against all their rivals. As
a consequence, Hybrida had to remove Cicero as a friend on Facebook. At hearing the news Cicero
was devastated. If filthy rich people like Crassus conspired against him, his chances of becoming a
consul of Rome were less than those of a crippled phoenician basket weaver surviving the 'rabid
wild animals matinee' Sunday afternoons in the amphitheatre.
Cicero knew, however, that the other aristocrats would never allow for all the power in the empire
to be concentrated in the hands of the conspirators. As a last resort he decided to turn to his old
nemesis Hortensius for help, in a desperate attempt to still get some support from the established
political families. At hearing the news of the conspiracy, Hortensius and his aristocratic friends
pretended not to be very impressed and sent Cicero home without much hope of any support.
The next day Cicero went to the elections a broken man, thinking the only thing he would win that
day was public humiliation. As the first results came in his fears were confirmed. Time after time
Catilina and Hybrida were the top ranking candidates, with Cicero dangling somewhere at the
bottom-end with the truly hopeless. Just when Cicero wanted to retire to his home and friends to
save what was still left of his dignity, Hortensius appeared. His nemesis made the public statement
of walking up to Cicero, who was standing on a stage with the other candidates. Without averting
his eyes from Cicero, Hortensius reached behind him and was handed his iPhone 4 by his personal
slave. With this powerful device he immediately wrote on his Twitter page “Cicero is a cool guy.
Just what Rome needs! Vote for him” (Figure 2). As you may know, Twitter profiles are public and
virtually everyone in Rome read Hortensius' message immediately. Many retweeted the message
spreading the word almost instantaneously to nearly every member of the electorate. Then
Hortensius removed his friendship with Hybrida and the other conspirators on Facebook, and many
of his friends did the same. As a result of Hortensius' public action all the remaining citizens who
could still vote swarmed to support Cicero, turning the tables in his favour.
And so it was that Cicero became consul of Rome as a new man without any family or financial
support, but thanks to Facebook and Twitter.
Social network analysis and Cicero
Undoubtedly this piece of fiction is not the story we want to write in the history books. Yet by
making the analogy with modern social media and exaggerating it to a ridiculous extent we can
imagine what the effects are of thinking about past social relationships through modern social
network analysis terminology. There are a number of problems related to using social network
analysis techniques for understanding social relationships in the past. Underlying these problems is
a specifically archaeological issue of the nature of archaeological data and how they reflect social
relationships in the past. These issues will be discussed in more detail below. But let us first explore
what social network analysis is through our example of Cicero's rise to power.
Social network analysis is used in the social and behavioural sciences as a set of theories, models
and applications that focus on the relationships among social entities, and on the patterns and
implications of these relationships. As such it cannot be seen as a single homogeneous method as its
name suggests. It is a distinct research perspective within the social and behavioural sciences,
however, because social network analysis is based on an assumption of the importance of
relationships among interacting units (Wasserman and Faust 1994, 3-4). In addition, social network
analysis applications have a number of principles in common, as summarized by Wasserman and
Faust (1994, 4):
Actors and their actions are viewed as interdependent rather than independent, autonomous
units
Relational ties (linkages) between actors are channels for transfer or 'flow' of resources
(either material or nonmaterial)
Network models focusing on individuals view the network structural environment as
providing opportunities for or constraints on individual action
Network models conceptualize structure (social, economic, political, and so forth) as lasting
patterns of relations among actors
These principles make the social network a useful perspective for understanding a diversity of
research questions including diffusion and adoption of innovations (Rogers 1979; Valente 1995;
Valente 2005), belief systems (Erickson 1988), markets (White 1981), exchange and power
(Markovsky et al. 1988) and occupational mobility (Breiger 1981). Its full potential for the
archaeological discipline has still to be explored (Brughmans 2010) and the discussion here of
issues surrounding the archaeological use of social network analysis can be seen as a step in this
direction.
Social network analysis methods are rooted in mathematics (in particular graph theory (Barnes and
Harary 1983; Harary 1969; Harary and Norman 1953), statistical and probability theory, and
algebraic models) from which techniques are adopted for identifying, examining and visualising
patterns of relationships. Visualization of social data is a crucial component of social network
analysis, as it facilitates an intuitive understanding of network concepts (Freeman 2005; Nooy et al.
2005, 14). A graph represents the structure of a network of relationships, while a network consists
of a graph and additional information on the vertices or the lines of the graph (Nooy et al. 2005, 6-
7). It consists of a set of vertices (also called points or nodes) which represent the smallest units in
the analysis, and a set of lines (or ties) between these vertices which represent their relationships.
Figures 3 to 6 are examples of social network visualisations. They show a minimal abstraction of
the evolving fictitious Facebook friendships of the Roman electorate as discussed in the short story
above. In these social networks individuals are represented by nodes and the lines between them
indicate friendship ties. On each network the location of Cicero is indicated. The nodes are given a
number to clearly distinguish the different groups within the Roman electorate mentioned in the
story. The new voters living north of Rome are the first group. Cicero and his closest friends are the
second group, and his supporters in Rome are the fifth. The aristocrats (including Hortensius,
Hybrida, Catilina, Crassus and Caesar) are the third and their supporters in Rome are the fourth
group.
A typical social network analysis of this evolving network would reveal a number of interesting
aspects of the structure of the Roman electorate's fictitious Facebook friendships, as well as the role
and position of individuals within this structure. At the start of the story (see Figure 3) Cicero was a
popular man with friends from different social groups in the city of Rome, including the aristocracy
thanks to his connection with Atticus. In this situation, however, Cicero's structural position is more
or less equal to his aristocratic rivals as he is influential to a similar number of people. Cicero has
the enormous disadvantage of not being strongly connected with the established aristocratic
political families. In this initial network we can also see that the communities to the north of Rome
are isolated, i.e. these individuals are not Facebook friends with any of the Roman voters. At this
stage neither Cicero or his rivals enjoy the support of these voters. Cicero thought this might have
been the case and decided to invest in these new voters. However, Cicero could not have done this
successfully on his own as he did not have friends in these communities. He had to ask some of his
friends to identify the most influential people and infiltrate the communities with their help. The
result can be seen in Figure 4. The northerners are now connected to the rest of the electorate and
Cicero became their 'bridge' to communities in the city of Rome. At this stage Cicero's position is
more favourable. Not only does he occupy a very central position in the entire social network,
having many friends himself and tying into many and diverse social groups, but he is also highly
influential to the social actions and flow of material and immaterial resources between the city
population and the northerners.
By losing his friendship with Hybrida, however, Cicero's position immediately becomes much less
favourable, as can be seen in Figure 5. He is still an important go-between for the northern
communities but became much more peripheral in the network as a whole. He has no direct support
from the aristocracy and has no influence on their actions, nor on the actions of their followers. The
tables turn when Hortensius publicly announces his support to Cicero, however (Figure 6). Cicero is
highly central in this network, being directly influential to all social groups within the electorate. He
dominates the flow of resources as a necessary link between most groups. Moreover, by losing their
friendship with the other aristocrats and their supporters, the conspirators become isolated and lose
all the influence they had on anyone outside their own group.
Some issues with social network analysis in archaeology
From this fictitious example of Cicero's evolving social networks we can tell that social network
analysis allows for a new look at old data, one that focuses on how relationships between
individuals or communities influence their actions and the flow of resources. It shows how Cicero's
initial network of friendships, although it was extensive, prohibited him from being elected as he
did not have relationships with the right groups of people. However, the networks illustrated how
this situation changed rapidly by forging a relationship with Hortensius, a man with many friends
and influential within the right social circles.
However interesting these conclusions might sound, they are not the results we wish to achieve with
our archaeological research efforts. Such statements do not fully explain social relationships
between people in the past for a number of reasons discussed below. I should stress that the issues
mentioned here are not exclusive to archaeology. Indeed, these and many more problems with social
network analysis are widely recognised in the social and behavioural sciences (Knox et al. 2006).
Archaeologists are, however, confronted with an additional complicating factor which sheds a
whole new light on these issues: the nature of archaeological data. As archaeologists our data
consists of the materialised remains of human actions, which by their very nature are fragmentary
samples of an unknown whole. Indeed, David Clarke (1973, 17) famously stated that 'Archaeology
… is the discipline with the theory and practice for the recovery of unobservable hominid behaviour
patterns from indirect traces in bad samples'. This is not to say that sampling issues are a purely
archaeological phenomenon. Data collection and sampling are no less problematic in the social and
behavioural sciences and require solid strategies (Frank 2005; Marsden 2005). Collecting data on
entire populations and defining the boundaries of such whole networks is considered particularly
problematic (Knox et al. 2006, 120-121; Marsden 2005, 9-10). However, the ability to interview
individuals in interaction and observe human behaviour directly is an advantage most of the social
sciences have over archaeology, until time travel becomes a possibility that is. The added challenge
of having to derive such human behaviour indirectly through material remains makes the
archaeological use of social network analysis techniques fundamentally different from that in other
disciplines. So let us have a look at some of the issues with using social network analysis as
archaeologists.
Firstly, social network analysis methods reduce the complexity of social interaction to a limited
number of variables. In the case of our story about Cicero, for example, to its attestation in one
medium: fictitious Facebook friendships. Relationships between individuals are shaped by a diverse
range of often overlapping factors. Individuals and social phenomena are embedded in a web of
social relationships (Granovetter 1985) which are themselves constantly negotiated against a
background of multiple contexts or domains (e.g. political, economic, cultural) (Mische and White
1998). In this sense, a social network itself is no more than one attribute of an individual who is also
related or affiliated to a number of contexts (Knox et al. 2006, 118; Watts 2003, 114-121). A single
network cannot capture this complexity of social relationships, especially in intimate networks
which by their very nature are multi-layered (Gamble 1999, 58). In addition, a network's
visualization as a graph contributes in part to this problem. The number of dimensions (or variables)
a graph can represent is limited. Indeed, 'the world is not a graph' as Knox et al. (2006, 135) rightly
put it. Being a potent exploratory tool, however, social network visualizations are quite influential
to the way social relationships are interpreted (Freeman 2005). Network analysts try to overcome
this issue in part by confronting multiple networks (Koehly and Pattison 2005), exploring them as
parts of 'whole networks' through popular network models (Barabási and Albert 1999; Watts and
Strogatz 1998), or by giving them a fuller cultural foundation (Mische and White 1998). We will
discuss the potential of these approaches for the archaeological discipline in more detail below.
Although a number of archaeological applications of network analysis are essentially limited to
exploring a single network (Graham 2006a; Isaksen 2008; Mizoguchi 2009), some archaeologists
and historians do stress the need to explore multiple facets of social relationships through
overlapping networks (Munson and Macri 2009; Preiser-Kapeller 2010). Munson and Macri (2009)
adopted a network approach to understand changing patterns of social and political interaction in
Classic Maya society. They drew upon an extensive epigraphic database to create networks with
101 sites as nodes and 1044 contextually defined place-name phrases or statements as arcs (directed
relationships) or loops (relationships from one node to itself). The renormalized degree
centralization index (Butts 2006) is used which, as it includes variables for network size and
density, allows one to compare networks of different types of relationships. Munson and Macri
confronted antagonistic, diplomatic, subordinate and kinship networks. Moreover, they rightly
stress that one of the advantages of network-based approaches is that they do not assume
geographical structure to be significant (Munson and Macri 2009, 429). One can ignore
relationships based on proximity and choose to explore the structure of different types of
relationships, or do both. Munson and Macri did just that. They confronted their networks with the
average geographic distance between paired sites for each sub-network, adding yet another layer to
an already complex web of interdependent relationships.
Munson and Macri's pioneering work on multiple archaeological networks is a first step towards
confronting the complex nature of social relationships embedded in evolving and diverse contexts.
But even if we represent all the archaeological data we have as a series of overlapping networks, we
would still not be able to understand this complexity because of the fragmentary nature of our
sources and our ignorance of the entire population our sample is derived from. Some aspects of
social relationships just do not leave a trace in the archaeological record. Moreover, social network
analysis does not help us to clearly identify those aspects our sources might potentially reveal to us.
What past social networks we think the data reflects is entirely up to the archaeologist, who is
oneself embedded in a complex web of social relationships within a set of contexts.
Secondly, following the argument voiced by Riles (2001) and Knox et al. (2006), recasting social
relationships in a network form also reveals the significant methodological issue that the
phenomenon we are interested in has essentially become the same things as the technique we use
(Riles 2001, 172). In Riles' (2001) words, it can be turned inside out: 'the inside of the networks (the
social relationships of which it is composed) is at the same time the outside (the representations or
visualisation)' (Knox et al. 2006, 133). By performing a social network analysis we assume that the
network is an existing social form which 'poses methodological dilemmas in relation to the
establishment of an analytical position' according to Knox et al. (2006, 115). Indeed,
anthropologists have shown that the network is an ethnographically significant form (Green 2002;
Riles 2001; Strathern 1996). However, keeping network-techniques and network-phenomena apart
as two separate things is crucial but not easy in practice, given the rigidity (and indeed the
institutionalisation as Knox et al. (2006, 115) call it) of established social network analysis
methods. The phenomena challenge this rigidity and reveal inherent assumptions social theorists
have imposed by using the network metaphor as an explanatory device (Knox et al. 2006, 134). In
fact, the use of network analysis as an explanatory tool is limited as I have argued elsewhere
(Brughmans 2010, 298). The social network metaphor should challenge questions of social
relatedness, but 'as soon as it stops challenging and starts prescribing, then the productive capacity
of the network is diminished' (Knox et al. 2006, 134).
Most archaeological applications of network analysis consider it as a set of ideas and methods for
analysis rather than an actual social form. In fact, many of them (e.g. Graham 2006b; Mizoguchi
2009) focus too much on network techniques without adequately discussing the implications these
techniques entail concerning networks as a social form. In these cases networks are seen as an
explanatory tool and the danger exists that the assumptions inherent in social network techniques
will be reflected in the interpretations. As I will argue below, an awareness of a healthy balance
between network techniques and phenomena should be explicitly present in archaeological
applications of social network analysis.
Thirdly, human actions are limited by a strictly local knowledge of the networks they belong to and
influenced by a general ignorance of the social system as a whole (Watts et al. 2002). In our
fictitious story for example, Cicero decided to win over new voters because he considered his own
social network too small, not because he knew the exact size and structure of his competitors'
networks and the impact his efforts would have on them. Also, Cicero did not have knowledge of
the social networks of these new voters. He had to rely on the local knowledge of other individuals
to direct his efforts for his campaign. This notion is crucial when we want to understand human
actions in a social network from an individual's point of view, as for the 'search in networks'
problem for example (for an overview see Watts 2003, 130-161). Imagine one individual is given
the task to pass an object on to another individual unknown to him or her (for the original
experiment see Korte and Milgram 1970; Milgram 1992). What the first person will do is pass the
object on to someone he or she believes to be closer (in any conceivable way: geographically,
socially, professionally, ...) to the recipient. What the first person will not do, however, is identify
the shortest possible path to deliver the message, as this individual does not have the necessary
knowledge of the social network as a whole to do this. This means that at every step in the process
of moving an object from one part of a social network to another, a decision will be made to act,
motived by an individual's local knowledge (Kleinberg 2000; Watts et al. 2002). This notion can be
explored through the well established distinction between 'whole network' and 'ego network'
methods in social network analysis (Knox et al. 2006, 118; Marsden 2002; Wellman 1988). The
former cover entire populations whilst the latter include only one node (or the 'ego'), its neighbours
and all lines among these selected nodes (Nooy et al. 2005, 145). A number of network analytical
techniques have been developed for both 'whole networks' (Wasserman and Faust 1994) and 'ego-
networks' (Marsden 2002).
These two distinct network perspectives allow for top-down as well as bottom-up approaches in
archaeology (Coward 2010, 458; Gamble 1999, 33-36; Earl et al. 2011). In light of the nature of
archaeological data, however, this limitation to local knowledge of the network becomes a
determining issue in the selection of a specific social network analysis approach. As archaeologists,
our data are typically the material residues of individuals' actions influenced by the local knowledge
of the social networks they were embedded in. It can only inform us of parts of social networks. As
we are not in a position to explore social relationships directly, but rather through its reflection in
material culture, exploring entire social networks as a patchwork of “local knowledge” becomes
problematic. Moreover, many social network analysis techniques assume knowledge of the entire
social network, like some of the popular centrality measures for example (Everett and Borgatti
2005; Freeman 1979). The selection of quantitative techniques should happen with the nature of the
data in mind. Munson and Macri (2008, 426-427) have argued that archaeological data are often not
fine-grained enough to identify humans as nodes. Although this might often be the case, and
without disregarding the potential for network-based approaches with larger entities (e.g. sites,
households, communities, artefact assemblages), I believe archaeologists should rise to this
challenge if we are to understand past social networks. The nature of our data demands it. All this
does not mean that top-down approaches are useless in archaeology. As Knappett et al. (2008) have
shown, confronting hypothetical social networks with the archaeological record is informative and
can lead to new ideas on how communities interacted. A combination of top-down and bottom-up
approaches is the most promising solution to this issue, as we will argue in more detail below.
Lastly, I would like to turn the discussion away from social network analysis for a brief moment and
reveal the wealth of potential archaeological use of 'the “new” science of networks' as it has been
termed by Duncan Watts (2004). This new field has emerged in recent years as the result of
interdisciplinary efforts and a mutual interest in the idea of networks in disciplines like physics,
sociology, mathematics, computer science, biology and economics. Of particular interest to
archaeologists are those network approaches that, contrary to social network analysis, do not require
social entities to be the nodes in the network. From methods to explore relations between physical
objects, like the system of linked routers that makes up the internet (Yook et al. 2002),
archaeologists can develop new approaches for understanding the many ways in which material
culture relates. Although archaeologists like Shawn Graham (2006b; 2009) and Jessica Munson
with Martha Macri (2009) have shown the potential of mainstream social network analysis
techniques for the archaeological discipline, I would like to stress that our interest in network
analysis should not be limited to its social application. Because, as I have argued elsewhere
(Brughmans 2010, 282), the danger exists that the insistence on humanizing networks will lead to
the misconception that all archaeological network analysis is social network analysis. That most
archaeological relationships have social implications is obvious, as archaeologists are concerned
with studying past human behaviour through an archaeological record that is itself created by
people. But archaeologists should not assume that the structure of such social implications is
examined directly through any type of network analysis. I will not discuss this point any further
here, as this paper is concerned with the potential and issues surrounding the archaeological use of
social network analysis techniques.
The problem
Social network analysis has been introduced in the archaeological discipline as a potent set of ideas
and tools to explore the social relationships between people in the past. The above discussion,
however, identified a few issues with this:
1. 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.
2. 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.
3. 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.
The key problem underlying these issues, and what makes archaeological applications of social
network analysis fundamentally different from its use in social and behavioural sciences, is the
nature of archaeological data. How does it reflect past social relationships? And can social network
analysis actually be used to visualise and explore these relationships, and to ultimately improve our
understanding of past social relationships? I argue that it can, as long as we keep the above
mentioned issues in mind. To do this, however, a significant modification of social network analysis
techniques is required and a specifically archaeological approach should be developed. In the
remainder of this paper I will make some suggestions as a first step towards solving this issue.
Thinking beyond the tool: the critical archaeological application of SNA
The first issue that needs to be addressed is the second one mentioned above: 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. At a very basic level, this statement
forces us to be thoroughly conscious about the social network analysis techniques we use and how
we use them. We do not want the network to become an aim in itself. Rather, it should be
considered a way to think about past social relatedness. However, applying a social network
analysis approach implies that we think through the metaphor of the network, which is itself a
cultural construction (Knox et al. 2006, 129). This is an obstacle we cannot avoid and all
archaeologists using network-based techniques should be conscious about.
When we continue following Riles' (2001) and Knox et al.'s (2006) arguments, we might confront
this issue by turning 'the network from the form of analysis to the focus of analysis and back again
to turn the network inside out in Riles' terms, in a self-reflective form of engagement' (Knox et al.
2006, 134). On a practical level this can be done by 'rather than begin[ning] with a whole
population defined by an organizational boundary, and using network methods to assess how this
population is structured, one starts from discursive unities in the form of stories to consider how far
they lead to organizational boundaries' (Knox et al. 2006, 130). As I mentioned above, in
archaeology we are only confronted with such particular stories, and they should form the basis of
our analyses. Archaeological networks can be built from particular aspects of social relationships
reflected in material remains. In doing so we allow the archaeological record to direct the creation
of networks. From there we need to think explicitly in terms of past social networks, and consider
how our archaeological networks inform us of different types of social networks that actually
existed in the past. From this, hypotheses of whole past social networks might emerge which can in
turn be submitted to a network analysis. This process of going from method to phenomena and back
again forces archaeologists to acknowledge the existence of the network metaphor and their specific
use of it.
The next issue, of individuals only having local knowledge of social networks, is probably the most
challenging to solve, as it puts strict limitations on the scale of past social networks archaeologists
are able to analyse. As we mentioned above, the resolution of archaeological data might very often
prevent us from getting the necessary information on a select group of individuals (Munson and
Macri 2009, 426-427), not mentioning the difficulties we are faced with when trying to find out
more than one single aspect of the social relationships of the same group of people. There are
examples, however, of archaeologists being able to reconstruct part of the social networks of
individuals. In his work on the Roman brick industry in central Italy, Shawn Graham (2006b; 2009)
combined information on brick producing centres, derived from an archaeometrical analysis of clay
sources, with names of individuals appearing on brick stamps. He was able to construct a social
network of individuals where Domitia Lucilla, mother of Marcus Aurelius, occupied a structurally
favourable position through which she was able to control the flow of information in the brick trade
(Graham 2006b, 93-114; 2009, 681). This type of data has the potential to explore individuals'
position on and knowledge of part of past social networks. Such a local approach is to be
encouraged in particular because we will never be informed on the entire population and their
relationships, the boundaries of these networks are always artificial creations as a direct
consequence of the archaeological record.
From data like this a true bottom-up approach can follow, where the individual's point of view is the
main focus. I believe ego-network techniques developed by social network analysts are most
promising. As mentioned earlier, these ego-networks only discuss the ego, its neighbours and the
relationships between them. In this way, the data we need to collect becomes much smaller and we
will be able to consciously treat archaeological data for what it is: material reflections of an
individual's actions motivated by local knowledge of the social networks. It allows us to compare
networks of the material evidence with hypothesised partial social (ego-)networks, to remain
conscious about the things we believe the archaeological record has to reveal to us.
But how about entire systems? What about the top-down approach? It should be stressed that an ego
approach and a whole network approach are not mutually exclusive (Marsden 2005). Bentley and
Maschner (2001, 38) argue that a complex systems approach does not remove the individual from
consideration or dehumanise change. The system implies that it is shaped by local action, just as
much as ego's with local knowledge imply that there is a wider social network. As I argued before,
it is problematic to add up parts of social networks to get a better idea of the system. I believe the
way we should approach this issue of how to link ego- and whole networks is the approach to
complex systems in archaeology as described by Bentley and Maschner (2001, 2003a). Underlying
this approach is the familiar idea that the whole is greater than the sum of its parts, which is at least
as old as Aristotle (Metaphysica Book VII, Ch. 10). The strength of a complex systems approach, as
Bentley and Maschner (2003b, 1) rightly argue, lies in its ability to 'offer a scientific method for
bridging the reductionist study of parts … to the constructionist study of the related whole'. As such,
it does not aim to understand every individual aspect of a complex system, as in complex systems
new properties and new behaviour appear constantly (Anderson 1972, 393). Rather, it specifically
considers complexity as being complex. This idea might sound self-evident but its relevance
becomes clear in light of the complexity of social relationships mentioned above. A complex
systems approach, I believe, might provide a powerful scientific method to make up for the
reductionist perspective of ego-networks in social network analysis and confront them with the
complex whole of which they are part of. In practice, archaeological networks can be confronted
with the known features and behaviour of popular complex systems models, like the scale-free
(Barabási and Albert 1999) and the small-world (Watts and Strogatz 1998) models (for overviews
see Albert and Barabási 2002; Barabási 2002; Newman et al. 2006; Watts 2003; Watts 2004).
Lastly we will discuss the first mentioned issue: 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. The key thing I argue to do is contextualise.
First of all, as many different aspects of past social relationships should be represented as networks.
This could be done through the creation of multiple networks as Munson and Macri (2009)
illustrated. Metrics to compare such multiple networks should be adopted from the social sciences
like those suggested by Koehly and Pattison (2005), and critically modified for the archaeological
discipline. However, this approach still leaves two issues unresolved. As I mentioned before, it still
leaves a big part of the complexity of social relationships unspoken for. And secondly, it does not
include the many contexts that I argued were so influential to shaping social networks. To tackle
this second issue, I suggest to develop an archaeological method for using affiliation networks. This
is a well-established approach in social network analysis where the membership of an organisation
or the participation in an event is considered a source of social ties. Affiliation networks are
typically represented as two-mode networks, where one group of nodes represents the
individuals/communities and another group the organisations. 'The affiliation network in fact
becomes a substrate on which the actual network of social ties is enacted' (Watts 2003, 118). In fact,
the affiliation network in archaeology can be used to map broad generic or small specific contexts
explicitly. Contexts could be broad known social, geographic or political entities. Stratigraphic
contexts or typologies could also be used as contexts in affiliation networks. A growing set of
metrics to analyse two-mode affiliation networks is being developed (Everett and Borgatti 2005;
Faust 2005).
The first issue of not being able to attest for the full complexity of social relationships is more
problematic, however. The multiple layers of social networks and the contexts with which they
engage are obviously not discrete entities but have a considerable degree of overlap. This overlap in
a sense is reflected through individuals having multiple affiliations, yet it is limited to the size of
our dataset. To deal with this issue, Mische and White (1998) argue that 'as we live in overlapping
and multiple networks, we need to focus on the “switching processes” in which we move from one
network to another. These are the “public” arenas in which multiple stories coexist and jostle
alongside each other' (Knox et al. 2006, 130). In archaeology this is easier said than done.
Comparing different aspects of past social relationships reflected in different data types already
involves plenty of issues. But to explore exactly how these different aspects relate to each other
requires data the archaeological record arguably cannot offer and introduces methodological
challenges social network analysis cannot help us with. Hypothetical network models of these
'switching processes' could be constructed and tested against our data. But other than hypothesising,
I believe that here the role of a social network approach for understanding past social relationships
ends.
Conclusion
In this paper I have argued that a social network analysis approach holds great potential for
understanding aspects of past social relationships. Social network theories, analytical techniques
and applications cannot be adopted in the archaeological discipline without question, however.
Three issues were revealed that make such an adoption problematic. Firstly, 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, 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. A key problem underlying these three issues, and what makes archaeological
applications of social network analysis fundamentally different from its use in social and
behavioural sciences, is the nature of archaeological data. I argued that if social network analysis is
to be of any use in archaeology these issues will need to be addressed and archaeological data
critique should be explicitly incorporated in its methodologies.
As a first step to solving these issues I have suggested a number of approaches that I believe are
particularly promising for archaeological applications. Firstly, we should turn the network from the
form of analysis to the focus of analysis and back again. This process of going from method to
phenomena and back again forces archaeologists to acknowledge the existence of the network
metaphor and their specific use of it. Secondly, an individual's perspective with only local
knowledge of social networks can be explored through ego-network techniques. A complex systems
approach on the other hand, could bridge the gap between ego-networks and the complex whole-
networks of which they are part. Such a combination of a bottom-up and top-down approach will
allow us to acknowledge the existence of the complex whole as well as the parts, whilst avoiding
the whole becoming merely a sum of its parts. Thirdly, we will need to contextualise explicitly by
confronting as many archaeological networks as possible, using techniques to compare multiple
networks. In addition, these multiple networks can be positioned within affiliation networks that
represent the many context with which past social relationships, but also the archaeological data and
the archaeologists engage.
Such an aggregated approach draws upon a number of network analytical techniques, a necessary
evil if we aim to understand past social relationships through a social network perspective. I have
argued, however, that we will never be informed about the full complexity of past social
relationships. Moreover, social network analysis techniques would not even succeed in
understanding this complexity if we would be informed about it. 'Facebooking the past' only makes
sense if one is aware of the issues involved.
Acknowledgement
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Figures
Figure 1: Cicero's Facebook profile.
Figure 2: Hortensius' Twitter profile announcing his support to Cicero.
Figure 3: minimal social network of fictitious Facebook friendships of the Roman electorate as
discussed in the short story, at the start of the story.
Figure 4: minimal social network of fictitious Facebook friendships of the Roman electorate as
discussed in the short story, after Cicero became friends with the most influential people of the
Northern communities.
Figure 5: minimal social network of fictitious Facebook friendships of the Roman electorate as
discussed in the short story, after Hybrida removed Cicero as a friend on Facebook.
Figure 6: minimal social network of fictitious Facebook friendships of the Roman electorate as
discussed in the short story, after Hortensius announced his support to Cicero.