When talking about the politics of the social Web and particularly online networking, the first issue coming up is invariably the question of privacy and its counterpart, surveillance – big brother, corporations bent on world dominance, and so on. My gut reaction has always been “yeah, but there’s a lot more to it than that” and on this blog (and hopefully a book in a not so far future) I’ve been trying to sort out some of the political issues that do not pertain to surveillance. For me, social networking platforms are more relevant to politics as marketing rather than surveillance. Not that these tools cannot function quite formidably to spy on people, but it is my impression that contemporary governance relies on other principles more than the gathering of intelligence about individual citizens (although it does, too). But I’ve never been very pleased with most of the conceptualizations of “post-disciplinarian” mechanisms of power, even Deleuze’s Post-scriptum sur les sociétés de contrôle, although full of remarkable leads, does not provide a fleshed-out theoretical tool – and it does not fit well with recent developments in the Internet domain.

But then, a couple of days ago I finally started to read the lectures Foucault gave at the Collège de France between 1971 and 1984. In the 1977-1978 term the topic of that class was “Sécurité, Territoire, Population” (STP, Gallimard, 2004) and it holds, in my view, the key to a quite different perspective on how social networking platforms can be thought of as tools of governance involved in specific mechanisms of power.
STP can be seen as both an extension and reevaluation of Foucault’s earlier work on the transition from punishment to discipline as central form in the exercise of power, around the end of the 18th century. The establishing of “good practice” is central to the notion of discipline and disciplinary settings such as schools, prisons or hospitals serve most of all as means for instilling these “good practices” into their subjects. Jeremy Bentham’s Panopticon – a prison architecture that allows a single guard to observe a large population of inmates from a central control point – has in a sense become the metaphor for a technology of power that, in Foucault’s view, is part of a much more complex arrangement of how sovereignty can be performed. Many a blogpost has been dedicated to applying the concept on social networking online.

Curiously though, in STP, Foucault calls the Panopticon both modern and archaic, and he goes as far as dismissing it as the defining element of the modern mechanics of power; in fact, the whole course is organized around the introduction of a third logic of governance besides (and historically following) “punishment” and “discipline”, which he calls “security”. This third regime is no longer focusing on the individual as subject that has to be punished or disciplined but on a new entity, a statistical representation of all individuals, namely the population. The logic of security, in a sense, gives up on the idea of producing a perfect status quo by reforming individuals and begins to focus on the management on averages, acceptable margins, and homeostasis. With the development of the social sciences, society is perceived as a “natural” phenomenon in the sense that it has its own rules and mechanisms that cannot be so easily bent into shape by disciplinary reform of the individual. Contemporary mechanisms of power are, then, not so much based on the formatting of individuals according to good practices but rather on the management of the many subsystems (economy, technology, public health, etc.) that affect a population so that this population will refrain from starting a revolution. Foucault actually comes pretty close to what Ulrich Beck’s will call, eight years later, the Risk Society. The sovereign (Foucault speaks increasingly of “government”) assures its political survival no longer primarily through punishment and discipline but by managing risk by means of scientific arrangements of security. This not only means external risk, but also risk produced by imbalance in the corps social itself.

I would argue that this opens another way of thinking about social networking platforms in political terms. First, we would look at something like Facebook in terms of population not in terms of the individual. I would argue that governmental structures and commercial companies are only in rare cases interested in the doings of individuals – their business is with statistical representations of populations because this is the level contemporary mechanisms of power (governance as opinion management, market intelligence, cultural industries, etc.) preferably operate on. And second – and this really is a very nasty challenge indeed – we would probably have to give up on locating power in specific subsystems (say, information and communication systems) and trace the interplay between the many different layers that compose contemporary society.

The concept of self-organization has recently made quite a comeback and I find myself making a habit of criticizing it. Quite generally I use this blog to sort things out in my head by writing about them and this is an itch that needs scratching. Fortunately, political scientist Steven Weber, in his really remarkable book The Success of Open Source, has already done all the work. On page 132 he writes:

Self-organization is used too often as a placeholder for an unspecified mechanism. The term becomes a euphemism for “I don’t really understand the mechanism that holds the system together.” That is the political equivalent of cosmological dark matter.

This seems really right on target: self-organization is really quite often just a means to negate organizing principles in the absence of an easily identifiable organizing institution. By speaking of self-organization we can skip closer examination and avoid the slow and difficult process of understanding complex phenomena. Webers second point is perhaps even more important in the current debate about Web 2.0:

Self-organization often evokes an optimistically tinged “state of nature” narrative, a story about the good way things would evolve if the “meddling” hands of corporations and lawyers and governments and bureaucracies would just stay away.

I would go even further and argue that especially the digerati philosophy pushed by Wired Magazine equates self-organization with freedom and democracy. Much of the current thinking about Web 2.0 seems to be quite strongly infused by this mindset. But I believe that there is a double fallacy:

1. Much of what is happening on the Social Web is not self-organization in the sense that governance is the result of pure micro-negotiations between agents; technological platforms lay the ground for and shape social and cultural processes that are most certainly less evident than the organizational structures of the classic firm but nonetheless mechanisms that can be described and explained.
2. Democracy as a form of governance is really quite dependent on strong organizational principles and the more participative a system becomes, the more complicated it gets. Organizational principles do not need to be institutional in the sense of the different bodies of government; they can be embedded in procedures, protocols or even tacit norms. A code repository like SourceForge.net is quite a complicated system and much of the organizational labor in Open Source is delegated to this and other platforms - coordinating the work effort between that many people would be impossible without it.

My guess is that the concept of self-organization as “state of nature” narrative (nature = good) is much too often used to justify modes of organization that would imply a shift power from traditional institutions of governance to the technological elite (the readers and editors of Wired Magazine). Researchers should therefore be weary of the term and whenever it comes up take an even closer look at the actual mechanisms at work. Self-organization is an explanandum (something that needs to be explainend) and not an explanans (an explanation). This is why I find network science really very interesting. Growth mecanism like preferential attachment allow us to give an analytical content to the placeholder that is “self-organization” and examine, albeit on a very abstract level, the ways in which dynamic systems organize (and distribute power) without central control.

This morning Jonah Bossewitch pointed me to an article over at Wired, authored by Chris Anderson which announces “The End of Theory”. The article’s main argument in itself is not very interesting for anybody with a knack for epistemology – Anderson has apparently never heard of the induction / deduction discussion and a limited idea about what statistics does – but there is a very interesting question lurking somewhere behind all the Californian Ideology and the following citation points right to it:

We can stop looking for models. We can analyze the data without hypotheses about what it might show. We can throw the numbers into the biggest computing clusters the world has ever seen and let statistical algorithms find patterns where science cannot.

One could point to the fact that the natural sciences had their experimental side for quite a while (Roger Bacon advocated his scientia experimentalis in the 13th century) and that a laboratory is in a sense a pattern-finding machine where induction continuously plays an important role. What interests me more though is Anderson’s insinuation that statistical algorithms are not models. Let’s just look at one of the examples he uses:

Google’s founding philosophy is that we don’t know why this page is better than that one: If the statistics of incoming links say it is, that’s good enough. No semantic or causal analysis is required.

This is a very limited understanding of what constitutes a model. I would argue that PageRank does in fact rely very explicitly on a model which combines several layers of justification. In their seminal paper on Google, Brin and Page write the following:

PageRank can be thought of as a model of user behavior. We assume there is a “random surfer” who is given a web page at random and keeps clicking on links, never hitting “back” but eventually gets bored and starts on another random page. The probability that the random surfer visits a page is its PageRank.

The assumption behind this graph oriented justification is that people do not randomly place links but they do so with purpose. Linking implies attribution of importance: we don’t link to documents that we’re indifferent about. The statistical exploration of the huge graph that is the Web is indeed oriented by this basic assumption and adds the quite contestable ruling according to which shall be most visible what is thought important by the greatest number of linkers. I would, then, argue that there is no experimental method that is purely inductive, not even neural networks. Sure, on the mathematical side we can explore data without limitations concerning their dimensionality, i.e. the number of characteristics that can be taken into account; the method of gathering data is however always a process of selection that is influenced by some idea or intuition that at least implicitly has the characteristic of a model. There is a deductive side to even the most inductive approach. Data is made not given and every projection of that data is oriented. To quote Fernando Pereira:

[W]ithout well-chosen constraints — from scientific theories — all that number crunching will just memorize the experimental data.

As Jonah points out, Anderson’s article is probably a straw man argument whose sole purpose is to attract attention but it points to something that is really important: too many people think that mathematical methods for knowledge discovery (datamining that is) are neutral and objective tools that will find what’s really there and show the world as it is without the stain of human intentionality; these algorithms are therefore not seen as objects of political inquiry. In this view statistics is all about counting facts and only higher layers of abstraction (models, theories,…) can have a political dimension. But it matters what we count and how we count.

In the end, Anderson’s piece is little more than the habitual prostration before the altar of emergence and self-organization. Just exchange the invisible hand for the invisible brain and you’ll get pop epistemology for hive minds…

When sites that involve any kind of ranking change their algorithm, there’ll probably be a spectacle worth watching. When Google made some changes to their search algorithms in 2005, the company was sued by KinderStart.com (a search engine for kids, talk about irony) who went from PageRank riches to rags and lost 70% of their traffic in a day (the case was dismissed in 2007). When Digg finally gave in to a lot of criticism about organized front page hijacking and changed the way story promotion works to include a measure of “diversity”, the regulars were vocally hurt and unhappy. What I find fascinating about the latter case was the technical problem-solving approach that implied the programming of nothing less that diversity. It’s not that hard to understand how such a thing works (think “anti-recommendation system” or “un-collaborative filtering”), but still, one has to sit back and appreciate the idea. We are talking about social engineering done by software engineers. Social problem = design problem.

The very real-world effects of algorithms are quite baffling and since I started to read this book, I truly appreciate the ingenuity and complex simplicity that cannot be reduced to a pure “this is what I want to achieve and so I do it” narrative. There is a delta between the “want” and the “can” and the final system will be the result of a complex negotiation that will have changed both sides of the story in the end. Programming diversity means to give the elusive concept of diversity an analytical core, to formalize it and to turn it into a machine. The “politics” of a ranking algorithm is not only about the values and the project (make story promotion more diverse) but also a matter - to put it bluntly - of the state of knowledge in computer science. This means, in my opinion, that the politics of systems must be discussed in the larger context of an examination of computer science / engineering / design as in itself an already oriented project, based on yet another layer of “want” and “can”.

Thanks to Joris for pointing out that my blog was hacked. Damn you spammers.

The term “determine” is often used rather lightly by those who write about the political dimension of technology. At the same time the accusation of “technological determinism” - albeit sometimes right on target - is being used as a means to exclude discussion of technological parameters from the humanities and the social sciences. But what is actually meant by “technological determinism”? In my view, there are three basic forms of thinking about determinism when it comes to technology:

The first is very much connected to French anthropologist André Leroi-Gourhan and holds that technological evolution is largely self-determined. His notion of “tendance technique” takes its inspiration from evolutionary theory in the sense that the technology evolves blindly but following the paths carved out by the “choices” made throughout its phylogenesis (this has been called “cumulative causation” or “path dependency” by some). Leroi-Gourhan’s perspective has been developed further by Deleuze and Guattari in their concept of “phylum” and, most notably, by philosopher of technology Gilbert Simendon (who’s work is finally going to be translated into English, hopefully still in 2008) who sees the process of technological evolution as “concretization”, going from modular designs to always more integrated forms. “Technological determinism” would mean, in this first sense, that technology is not the result of social, economic, or cultural process but largely independent, forcing the other sectors to adapt. Technology is determined by its inner logic.

A more colloquial meaning of technological determinism is, of course, connected to the Toronto school, namely Harold A. Innis and Marshall McLuhan. This stuff is so well known and overcommented that I don’t really want to get into it – let’s just say that technology, here, determines social process either by installing a specific rapport to covering space and time (Innis) or by establishing a certain equilibrium of the senses (McLuhan). You can find dystopical versions of the same basic concept in Ellul or Postman: technology determines society, to state matters bluntly.

I would argue that there is third version of technological determinism which is, although not completely dissimilar, far more subtle than the last one. Heidegger’s framing of technology as Gestell (an outlook based on cold mathematical reasoning, industrial destruction of more integrated ways of living, exploitation of nature, etc.) opens up a question that has been taken up by a large number of people in design theory and practice: is technology determined to follow the logic of Gestell? In Heidegger’s perspective, technology is doomed to exert a dehumanizing force on being itself: the determinism here does not so much concern the relationship between technology and society but the essence (Wesen) of technology itself. A lot of thinking about design over the last thirty years has been based on the assumption that a different form of technology is possible: technology that would escape its destiny as Gestell and be emancipating instead of alienating. Discourse about information technology is indeed full of such hopes.

Although “technological determinism” refers most often to the second perspective, a closer examination of “what determines what” opens up a series of quite interesting questions that go beyond the vulgar interpretations of McLuhan’s writings. For those who still adhere to the idea that tools determine their use, here is a list of possible remedies:

1. Look at design studies where determinism has been replaced by the quite elegant notion of affordance.
2. Read more Actor-Network Theory.
3. Think about what Roland Barthes meant by “interpretation”.
4. Dust off your copy of Hall’s “encoding/decoding”.
5. Work as a software developer and marvel at the infinity of ways users find to use, appropriate, and break your applications.

Two things currently stand out in my life: a) I’m working on an article on the relationship between mathematical network analysis and the humanities, and b) continental Europe is finally discovering Facebook. The fact that A is highly stimulating (some of the stuff I’m reading is just very decent scholarship, especially Mathématiques et Sciences humaines [mostly French, some English] is a source of wonder) and B quite annoying (no, I don’t miss kindergarten) is of little importance here; there is, however, a connection between the two things that I would like to explore a little bit here.

Part of the research that I’m looking into is what has been called “The New Science of Networks” (NSN), a field founded mostly by physicists and mathematicians that started to quantitatively analyze very big networks belonging to very different domains (networks of acquaintance, the Internet, food networks, brain connectivity, movie actor networks, disease spread, etc.). Sociologists have worked with mathematical analysis and network concepts from at least the 1930ies but because of the limits of available data, the networks studied rarely went beyond hundreds of nodes. NSN however studies networks with millions of nodes and tries to come up with representations of structure, dynamics and growth that are not just used to make sense of empirical data but also to build simulations and come up with models that are independent of specific domains of application.

Very large data sets have only become available in recent history: social network data used to be based on either observation or surveys and thus inherently limited. Since the arrival of digital networking, a lot more data has been produced because many forms of communication or interaction leave analyzable traces. From newsgroups to trackback networks on blogs, very simple crawler programs suffice to produce matrices that include millions of nodes and can be played around with indefinitely, from all kinds of angles. Social network sites like Facebook or MySpace are probably the best example for data pools just waiting to be analyzed by network scientists (and marketers, but that’s a different story). This brings me to a naive question: what is a social network?

The problem of creating data sets for quantitative analysis in the social sciences is always twofold: a) what do I formalize, i.e. what are the variables I want to measure? b) how do I produce my data? The question is that of building a representation. Do my categories represent the defining traits of the system I wish to study? Do my measuring instruments truly capture the categories I decided on? In short: what to measure and how to measure it, categories and machinery. The results of mathematical analysis (which is not necessarily statistical in nature) will only begin to make sense if formalization and data collection were done with sufficient care. So, again, what is a social network?

Facebook (pars pro toto for the whole category qua currently most annoying of the bunch) allows me to add “friends” to my “network”. By doing so, I am “digitally mapping out the relationships I already have”, as Mark Zuckerberg recently explained. So I am, indeed, creating a data model of my social network. Fifty million people are doing the same, so the result is a digital representation of the social connectivity of an important part of the Internet-connected world. From a social science research perspective, we could now ask whether Facebook’s social network (as database) is a good model of the social network (as social structure) it supposedly maps. This does, of course, depend on what somebody would want to study but if you ask yourself, whether Facebook is an accurate map of your social connections, you’ll probably say no. For the moment, the formalization and data collection that apply when people use a social networking site does not capture the whole gamut of our daily social interactions (work, institutions, groceries, etc.) and does not include many of the people that play important roles in our lives. This does not mean that Facebook would not be an interesting data set to explore quantitatively; but it means that there still is an important distinction between the formal model (data and algorithm, what? and how?) of “social network” produced by this type of information system and the reality of daily social experience.

So what’s my point? Facebook is not a research tool for the social sciences and nobody cares whether the digital maps of our social networks are accurate or not. Facebook’s data model was not created to represent a social system but to produce a social system. Unlike the descriptive models of science, computer models are performative in a very materialist sense. As Baudrillard argues, the question is no longer whether the map adequately represents the territory, but in which way the map is becoming the new territory. The data model in Facebook is a model in the sense that it orients rather than represents. The “machinery” is not there to measure but to produce a set of possibilities for action. The social network (as database) is set to change the way our social network (as social structure) works - to produce reality rather than map it. But much as we can criticize data models in research for not being adequate to the phenomena they try to describe, we can examine data models, algorithms and interfaces of information systems and decide whether they are adequate for the task at hand. In science, “adequate” can only be defined in connection to the research question. In design and engineering there needs to be a defined goal in order to make such a judgment. Does the system achieve what I set out to achieve? And what is the goal, really?

When looking at Facebook and what the people around me do with it, the question of what “the politics of systems” could mean becomes a little clearer: how does the system affect people’s social network (as social structure) by allowing them to build a social network (as database)? What’s the (implicit?) goal that informs the system’s design?

Social networking systems are in their infancy and both technology and uses will probably evolve rapidly. For the moment, at least, what Facebook seems to be doing is quite simply to sociodigitize as many forms of interaction as possible; to render the implicit explicit by formalizing it into data and algorithms. But beware merry people of The Book of Faces! For in a database “identity” and “consumer profile” are one and the same thing. And that might just be the design goal…

I have admired the work of Geoffrey Bowker and Susan Leigh Star for quite a while, especially their co-authored book Sorting Things Out is a major step towards understanding how systems of classification structure fields of perception and, consequently, action. The study of advanced technology is intrinsically related to information handling (in the largest sense, ranging from human cognition to information science): building categories, models, languages, and metaphors is a major part of designing information systems and with the ongoing infiltration of society by IT, the process of formalization (i.e. the construction of analytical categories that translate our messy world into manageable symbolic representations) has become a major difficulty in many software projects that concern human work settings. Ontology is ontology indeed but very often “reality as phenomenon” does resist being turned into “reality as model” – our social world is too complex and incoherent to fit into tidy data models. The incongruity between the two explains why there are so many competing classifications, models, and theories in the humanities and social sciences: no single explanation can claim to adequately cover even a small section of the cultural world. Our research is necessarily cumulative and tentative.

The categories and models used to build information systems are only propositions too, but they are certainly not (only) descriptive in nature. There is peculiar performativity to information structures that are part of software because they do not only affect people on the level of “ideas have impacts”. A scientific theory has to be understood, at least in part, in order to make a difference. When PageRank, which is basically a theory on the production of relevancy, became an implemented algorithm, there was no need for people to understand how it worked in order for it to become effective. Information technology relies on the reliable but brainless causality of the natural world to in-form the cultural world.

Why am I writing about this? The University of Vienna (my first alma mater) is organizing a workshop [german] on search engines before Google. And “before” should be read as “before digital technology” (think “library catalogue”). This is a very good idea because instead of obsessing about the “effects” that IT might have (or not) on “society” I believe we should take a step back and look at the categories, models, and theories that our information technologies are based on. And as a first step that means going back in time and researching the intellectual genealogy that is behind these nasty algorithms. The abstract I sent in (four days late, shame on me) proposes to look at early developments in bibliometrics that lead to the development of impact analysis, which is the main inspiration for PageRank.

The proposal is part of this project on mathematics and the humanities that I’m fantasizing about, but that’s a story for another day.

I have recently been thinking quite a lot about what it means to be “critical”. At a lot of the conferences I go to, the term is used a lot but somehow it remains intuitively unintelligible to me. The dictionary says that a critical person would be “inclined to judge severely and find fault” and a critical reading “characterized by careful, exact evaluation and judgment”. I cannot shake the impression that a lot of the debate about the political and ethical dimension of information systems is neither careful, nor exact. Especially when it comes to analyzing the deeds of big commercial actors like Google, there has been a pointed shift from complete apathy to hysteria. People like Siva Vaidhyanathan, whose talk about the “googlization of everything” I heard at the New Network Theory Conference, are, in my view, riding a wave of “critical” outrage that seemingly tries to compensate for the long years of relative silence about issues of power and control in information search, filtering, and structuration. But instead of being careful and exact - apparently the basis of both critical thought and scholarly pursuit - many of the newly appointed Emile Zolas are lumping together all sorts of different arguments in order to make their case. In Vaidhyanathan’s case for example, Google is bad because its search algorithms work too well and the book search not well enough.

Don’t get me wrong, I’m not saying that we should let the emerging giants of the Web era off the hook. I fully agree with many points Jeffrey Chester recently made in The Nation - despite the sensationalist title of that article. What I deplore is a critical reflex that is not concerned with being careful and exact. If we do not adhere, as scholars, to these basic principles, our discourse loses the basis of its justification and we are doing a disservice to both the political cause of fighting for pluralism of opinion in the information landscape and the academic cause of furthering understanding. Our “being critical” should not lead to obsession with the question of whether Google (or other companies for that matter) are “good” or “bad” but to an obsession about the more fundamental issues that link these strange systems that serve us the Web as a digestible meal to matters of political and economic domination. I’ve been reading a lot recently about how Google is invading our privacy but very little about the actual social function of privacy, seen as a historical achievement, and how the very idea could and should be translated into the information age where every action leaves a footprint of data waiting to be mined. We still seem to be in a “1984″ mindset that, in my view, is thoroughly misleading when it comes to understanding the matters at hand. If we phrase the challenges posed by Google in purely moral terms we might miss the ethical dimension of the problem - ethics understood as the “art of conduct” that is.

This might sound strange, but under the digital condition the protection of privacy faces many of the same problems as the enforcement of copyright, because they both concern the problem of controlling flows of data. And whether we like it or not, both technical and legal solutions to protecting privacy might end up looking quite similar to the DRM systems we rightfully criticize. It is in that sense that the malleability of digital technology throws us back to the fundamentals of ethics: how do we want to live? What do we want our societies to look like? What makes for a good life? And how do we update the answers to those questions to our current technological and legal situation? Simply put: I would like to read more about why privacy is fundamentally important to democracy and how protection of that right could work when everything we do online is prone to be algorithmically analyzed. Chastising Google sometimes look to me like actually arguing on the same level as the company’s corporate motto: “don’t be evil” - please?

We don’t need Google to repent their sins. We need well-argumented laws that clearly define our rights to the data we produce, patch up the ways around such laws (EULAs come to mind) and think about technical means (encryption based?) that translate them onto the system level. Less morals and more ethics that is.

I have been working, for a couple of month now, on what has been called “network theory” - a rather strange amalgam of social theory, applied mathematics and studies on ICT. What has interested me most in that area is the epistemological heterogeneity of the network concept and the difficulties that come with it. Quite obviously, a cable based computer network, an empirically established social network and the mathematical exploration of dendrite connections in worm brains are not one and the same thing. The buzz around a possible “new science of networks” (Duncan J. Watts) suggests, however, that there is enough common ground between a great number of very different phenomena to justify the use of similar (mathematical) tools and concepts to map them as networks. The question of whether of these things (the Internet, the spreading of disease, ecosystems, etc.) “are” networks or not, seems of less importance than the question of whether network models produce interesting new perspectives on the areas they are being applied to. And this is indeed the case.

One important, albeit often overlooked, aspect of any mathematical modeling is the question of formalization: the mapping of entities from the “real” world onto variables and back again, a process that necessarily implies selection and reduction of complexity. This is a first layer of ambiguity and methodological difficulty. A second one has been noted even more rarely, and it concerns software. Let me explain: the goal of network mapping, especially when applied to the humanities, is indeed to produce a map: a representation of numerical relations that is more intuitively readable that a matrix. Although graph (or network) theory does not need to produce a graphical representation as its result, such representations are highly powerful means to communicate complex relationships in a way that works well with the human capacity for visual understanding. These graphs, however, are not drawn by hand but generally modeled by computer software, e.g. programs like InFlow, Pajek, different tools for social network analysis, or a plethora of open source network visualization libraries. It may be a trivial task to visualize a network of five or ten nodes, but the positioning of 50 or more nodes and their connections is quite a daunting task and there are different conceptual and algorithmic solutions to the problem. Some tool use automatic clustering methods that lump nodes together and allow users to explore a network structure as hierarchical system where lower levels only fold up by zooming in on them. Parabolic projection is another method for reducing the number of nodes to draw for a given viewport. Three-dimensional projections represent yet another way to handle big numbers of nodes and connections. Behind these basic questions lurk matters of spatial distribution, i.e. the algorithms that try to make a compromise between accurate representation and visual coherence. Interface design adds yet another layer, in the sense that certain operations are made available to users, while others are not: zooming, dragging, repositioning of nodes, manual clustering, etc.

The point I’m trying to make is the following: the way we map networks is in large part channeled by the software we use and these tools are therefore not mere accessories to research but indeed epistemological agents that participate in the production of knowledge and participate in shaping research results. For the humanities, this is, in a sense, a new situation: while research methods based on mathematics are nothing new (sociometrics, etc.), the new research tools that a network science brings with it (other examples come to mind, e.g. data-mining) might imply a conceptual rift where part of the methodology gets blackboxed into a piece of software. This is not necessarily a problem but something that has to be discusses, examined, and understood.