So now for something completely different, I'm going to talk about networks.
First of all, I'd like to thank the DH organizers though for accepting what is really my first
DH presentation and my first attendance of this amazing conference.
And I would also like to thank the ADHO for facilitating my presence here through a bursary.
And lastly, I'd like to thank all of you guys because it's the last long paper of the day.
And I promise it's going to be extremely fascinating.
Although I hope to convince you.
So networks, I'm interested in networks and I'm specifically interested in how formal
network methods have been used by archaeologists and their potential for addressing specific
archaeological research questions and dealing with the limitations of archaeological data.
To do that, I did my homework.
I had to do a close reading of all the archaeological applications I was aware of.
And that led to a couple of intuitive conclusions.
And what I will be talking about today is how to express those intuitive conclusions
and evaluate them through another more quantitative approach, which is citation network analysis.
Because I'm interested in how these formal methods emerged in the archaeological discipline.
What influenced the archaeologists of actually using these things?
Which specific publications did they refer to when adopting things?
How did they adapt it within the archaeological discipline?
And how did it evolve?
I thought citation network analysis would be interesting for that.
On the other hand, it will also hopefully allow me to say something about how good the available
citation databases are for evaluating a process of influence within the archaeological discipline.
And maybe for the arts and humanities in general.
But this case today will be about archaeology.
So, as I said, I'm interested in archaeologists using networks.
And that's basically represented right here.
That's my homework.
It's chronology from 1960 till the present.
And on that I plotted all of the archaeological publications that use formal network methods.
The ones in bold are not archaeological ones.
They are publications from other disciplines that were just key to the development of formal network methods.
Now, you can see that ever since the 1960s, 1970s, archaeologists have been interested in networks,
have used it in some way.
A bit less in 1980s, 1990s.
But at the end of the 1990s, we see something happening.
There's two publications by physicists that pitched a couple of very popular models.
A small world network model and a scale-free network model.
And although social network analysts have been playing around with networks for at least the last 50 years,
it is only after the publication of these two works that we get something like this.
Archaeologists all of a sudden are way more interested in using formal network methods.
Half of them use those popular physics models.
But the other half refer to the social network tradition which has been around for a while.
If you just count the number of publications per year, you get something like this.
It's an interesting pattern and I'm interested in explaining that pattern.
I want to know why there's this peak in the number of publications in the last 10 years.
Why there were archaeologists using it 50 years ago or 40 years ago.
But it never really caught on after that.
And I thought a citation approach would be interesting.
Now, my close reading, I actually read all of those things.
I came up with a couple of intuitive results.
And they're basically that the archaeological applications of graph theory,
in which social network analyst techniques are very often based,
was not influential to more recent archaeological network studies.
And that potential of social network analysis for archaeology
was already kind of explored in the 1970s through mainly theoretical network models.
But a lot of those models actually identified specific approaches
that have only been applied in the last 10 years by archaeologists,
or have not been applied yet but show clear potential. Why is that?
Actually, social network analysis and social physics have by far been the most influential research traditions.
But only a few of those models and techniques have been applied by archaeologists.
And then there's a tendency towards a standardized explanation of a tested network structure,
which I won't be talking about now.
But all of these other intuitive conclusions,
what I want to do with them is express them in a quantitative way,
see whether it's actually represented in a formal expression of academic influence,
which is citations.
So my research question for this specific case study is,
can the intuitive results from close reading be quantitatively expressed and validated?
And can this network analysis provide new insights and citation behavior
within that limited bibliographical corpus of the archaeologists doing stuff?
But also, can I take a step back? Can I try to understand
how this process of influence of archaeologists is embedded within a wider multidisciplinary context?
How does the use of networks by archaeologists compare to the use of networks in social network analysis
or by physicists and where exactly did that influence come from?
It is a question. It's not a fact that citation network analysis is interesting for these specific questions.
Now, data and method.
In the first instance, to create my citation network,
I focused on 70 archaeological applications of formal network methods,
the ones I was aware of there here.
I called them the 70 egos, for reasons I will become clear later.
Now, I extracted all of the bibliographical data for those 70 egos from Web of Knowledge,
or I had to do it manually, which is a lot of work, and I would not recommend it to anyone.
But if you have the luck of finding some of these publications of Web of Knowledge,
this is where you get, you have information on the author, the title, the journal,
sometimes an abstract, but you definitely have a full list of all the citations within that publication.
And for 30 of them, I found that. For 40 of them, I had to extract it manually.
Now, from that data, you can make a citation network like this.
You represent a publication as a node, and you represent citation from that publication
to another publication in its bibliography by an arc.
If you do that for all of them, all of those 70 egos, then you get something as in picture number C.
But that is only just kind of adding up star-shaped networks.
What we don't have in this instance, something very important is those citations with a question mark.
So we don't know whether the first neighbors of those 70 egos actually cited each other,
which is very important to understand the kind of context within this was embedded.
So I needed to do more work. I needed to find the bibliographies of those first neighbors of the 70 egos.
So I made some kind of ego network, which basically represents the 70 egos,
their direct citations, and the citations among them.
To do that, I had to do a lot of work again.
So a couple of numbers here. 70 egos are the core, are the seed of the citation network.
They collectively cite 2,234 other publications.
Only 575 of these could actually be found on Web of Knowledge,
which means that there's no citation data within this case study for 1,660 other publications of direct citation of those egos.
So this kind of touches upon a bias within Web of Knowledge itself.
Obviously, archaeology and how archaeologists use network methods is my case study,
something I'm particularly interested in.
But it does reflect that, at least for this case study,
the archaeological, the citation databases are not very representative.
Anyways, just looking at the bibliographies of those 575 publications,
they collectively cite 33,000 publications and connected by 42,000 citations.
That's a lot of stuff, even though I've only got one-fourth of the actual kind of formal citation network of which these egos are part.
So it's still useful to have a look at that.
And I chose to take an approach that works on three different levels following the work of Max Schich and Michaela Gocia,
where in the first instance, because my close reading was just focusing on those archaeological applications,
I created a network of the archaeological egos, 70 nodes connected by 133 arcs,
and there's a couple of numbers there that say something about the networks if you like those things.
Then, on the second level, we're looking at the ego network.
The 70 egos, their direct citations and citations between them, 2,300 nodes, and then the bigger picture.
Basically, a data dump of everything I have of those 575 publications that I could find on Web of Knowledge.
And we're just going to look into those things, those networks, explore them on different levels and see what they tell us.
First of all, I have to be clear that what I consider a citation to be is something that people in literature seem to agree on
at the very least, and it's a formal representation of influence through publication.
So it doesn't say anything about how someone was influenced or the nature of that influence.
It doesn't say anything about the positive nature, the negative nature.
For that, you actually have to read this stuff.
It doesn't say much about the presence or absence of citations,
although within a citation network, obviously the absence of a link between two nodes is also data.
But the consistent absence of citation by a group of scholars, for example,
of one author who is not very much liked, which actually happens, cannot be evaluated with this data set, obviously.
So it has its limitations, and then there's two strong biases.
On the one hand, the Web of Knowledge bias, which journals actually includes which it doesn't.
For example, which is a big problem, until recently it didn't include books,
but also it only includes journals with a very strong editorial policy,
in a tradition of a strong editorial policy, that work on an international level.
A lot of the more local journals are very sub-discipline-specific journals
that actually matter for the people working in that sub-discipline.
The only things they read are not included in many cases.
And archaeology is, in my eyes, but any discipline could be a very good case study.
Archaeology is one, because in every region where there is archaeology,
people publish in specific regional journals that, in many cases,
like Pacific archaeology, for example, are not included in this citation data set.
So you can make also other networks, like co-authorship,
linking up keywords or subject categories,
assigned by Web of Knowledge or by the journals,
and then you can also look at the distribution of the number of journals present in this database.
I'll just be looking at the citation network in this case study.
Now, before we go on to actual results, citation networks are acyclic,
and that's very important when identifying which methods are useful and how to interpret them.
That basically means there are no closed loops of directed edges.
If you just hear that, that probably doesn't mean anything.
But really, this is a really good example here.
On the one hand, you have a representation of worldwide web,
a small structure, on the other hand, publication citation network.
One web page, for example, can cite a second web page,
which can cite a third web page, which can then cite back to the first web page,
because web pages can be updated constantly.
You can't have that with publications.
Publications from 2005 can cite a publication of 1995,
which can cite a publication of 1985,
which really can't cite a publication of 2005 unless they master time travel.
If you accept that acyclic nature, which is enforced in my case study,
where it matters depending on the methods,
you can identify a couple of methods that are particularly interesting.
The ones that are relevant for the results I'll describe in more detail once we get to that.
This is the free software, the open source software I use to actually do everything.
That's data and method.
Now, let's look at some results.
As I said, we're looking at this really big citation network on three different levels.
Let's zoom in.
The bigger picture, the ego networks, and then the archaeological applications.
That's the first thing we'll look into.
70 archaeological egos linked by 133 citations.
If you just explore that visually, you plot it chronologically,
you get something like this, and immediately you can't...
It looks like a boat, in my eyes.
But you can also see that that pattern of publication counts as represented here.
There's more publications in recent years than there are in the previous ones.
But what is not directly evident from the close reading is the difference in density
between those groups of publications.
The citations between publications last 10 years is way more dense.
There's very few links going back to the older publications,
and there's a very good reason for that.
One way to explore that density is through this measure, the input domain,
which I found particularly interesting.
Because it gives a score to every node,
depending on how many other nodes can lead to it by a path of citation.
Which means that publications that are older
will have a higher probability of having a higher score.
They will be emphasized with this measure
because they have a higher probability of accumulating
by whatever kind of path nodes that can lead to it.
So we get something like this.
And actually, it is not reflecting what I was expecting.
The older publications do have a high score,
but they don't have a significantly highest score,
and they don't really have the highest score within this network.
Instead, it's the publications at the start of that boom in the early 2000s
that have the highest score.
And these are the publications that pioneered
the adoption of small-world and scale-free network models in archaeology.
I thought that was interesting.
Then, we can look at different types of network projections.
One is a co-citation approach,
where you consider two papers to be co-cited
if they are both cited by the same third paper.
So in this example, publication A and publication B
are both cited by three other papers,
which means that in the co-citation projection,
they have an edge value of 3.
And you can do exactly the same, but the other way around,
which is called bibliographic coupling.
Two papers are said to be bibliographically coupled
if they cite the same other paper.
So A and B have in the bibliographic coupling projection
an edge weight of 3, because they are both citing three of the same papers.
Now, you get something like this.
And actually, again, this didn't really reflect my initial idea.
So I was hoping to get something more fragmentary.
This is a relatively dense network, where you've got a clear core
and a couple of peripheral nodes.
But, I mean, archaeologists are citing other archaeologists.
There wasn't a big divide,
until I looked into it in a bit more detail.
Because as I said, there's this big bias of me basically
identifying all of the archaeologists that are relevant in the publications.
So I have to kind of take myself out of the equation.
Also, I looked at the three most prominent nodes within this network,
and they just turned out to be very recently published
Reviews of Formal Network Analysis.
Take out the reviews and see what happens.
We get something like this.
The network starts to completely fragment,
and we have just a few islands of archaeologists citing other archaeologists,
but not really having a very broad knowledge of all these formal network methods
that are used within the archaeological discipline.
If we do the same for the bibliographic coupling,
again, we get some kind of relatively dense structure with a clear core.
Take the reviews out, and you get something like this.
Highly fragmented.
And that kind of gave rise to me, to the idea that archaeologists
using network methods aren't very well-networked themselves.
They don't seem to be referring to other archaeologists
using similar methods to formalize their influence by them.
Instead, they must have been influenced by something else,
by publications outside of the archaeological discipline,
which obviously makes sense.
But I thought this structure would be more dense, more connected.
Instead, it's quite interesting, actually.
You do see links between authors citing their own other papers
about the same topic that they previously published,
and also authors citing other authors within the same sub-discipline in archaeology.
And that's why I give the example of Pacific archaeology.
There's a couple of archaeologists working with networks in Pacific archaeology,
and they sometimes cite each other.
And sometimes they don't cite each other because of the use of formal network methods,
because to them, that's only kind of a side interest.
That's the method they use.
But they are interested in the archaeological results.
So just focusing on the evolution of a method through citation is quite interesting, actually.
So just on this local level, let's read out some conclusions.
On the one hand, this intuition I had from the close reading is more or less confirmed.
We see that there have been network methods being used by archaeologists since the 1970s,
but there was a booming application in the last decade,
and the new applications were hardly influenced by the older ones
as shown by the input degree measure.
And archaeologists did not seem to have been aware of the diverse range of existing applications.
Recently published reviews, when you take them out of the equation,
make that the network completely defragments.
So the academic context of the publication clearly plays an important role as well,
as I give the example with the Pacific archaeologists.
But where did the new applications get their ideas and techniques from,
if not from other archaeologists?
If I can make a point about the local level, it's this.
But now I'm stepping away from the close reading.
Now I'm stepping away from the 70 archaeologists that I'm very familiar with,
and we're embedding that within a wider network of citations.
We're looking at the ego network now.
So as I said, 70 egos connected by their direct citations,
directly cited publications, and the citations between them.
2,300 publications connected by almost 5,000 citations.
Again, we get a boat structure if we plot that chronologically,
which is completely meaningless.
But we do see that, obviously, there is publications that date way back,
but the densest core of citations within this network is sent from the 1960s up till the present.
I think this is quite interesting, because in some way, in the very least,
it must express in some way the emergence of and the increasing formalization
of social network analysis methods and the social network analysis community
that started to become more formalized in the 1960s, 1970s,
through the emergence of two new journals and international conferences.
So I think this doesn't only reflect people using networks, it's everyone they actually cite,
but I think it does reflect that increasing popularity and use of network methods to some degree.
Now, let's look at the indegree, which is a number of incoming citations a node receives.
Then you get a very confusing spaghetti monster and a nice distribution
that tells us that most of the nodes, most of the publications,
actually only get zero to one citation, where there's a chosen few that get way more publications,
probably some kind of rich-get-richer structure where everyone cites this very, very famous publication
that you just have to cite.
If you look at that in more detail, we get this ranking of indegree.
So the first two are basically those physics papers
that publish the scale-free and the small-world network model that I said was influential.
So I'm not saying anything new now, but it is interesting that it pops out.
Thirdly, we do get social network analysts appearing as well,
and mainly textbooks and popular theories in social network analysis,
like Granovetter's Strength of Weak Ties.
Archaeologists are very low in the ranking,
and if you compare this with Web of Knowledge and Google Scholar citations,
you do see that these people, they're not just influential to archaeologists, like this network shows,
they're also influential within their own discipline or within multidisciplinarity.
They've got a lot of citations, they should be very happy about that.
Now, let's look at the input domain measure,
and remember that last time we did not get the older publications that really stood out.
Here, the results are completely different.
We do get publications that are very old and that were actually very influential to
mainly physicists, like Zipp's Law from 1949, for example,
or the Erdos and Rennie 1960 random network model.
So, we do get a completely different pattern here than we get for the local scale network.
And again, the co-citation network looks scary, but very interesting.
What we have here is, because of the nature of this co-citation projection,
we have clusters that can be identified by a community detection algorithm on these networks,
clusters that are grouped together because they are similar in some way,
because they are similar because they are published in the same kind of discipline,
they're dealing with the same kind of topics, they're published in the same types of journals,
this is really a goldmine for me because I can look at where the archaeologists are in this network,
and what kind of community they belong to, and what are the other communities that
overlap more or less with these archaeologists,
and also, what are the communities that still use networks,
that talk about networks in some way, but are completely on the opposite side of archaeologists.
So, this is basically a map for me to kind of look into and trace those publications
that have not influenced archaeologists, but were prominent within their own disciplines.
I think that's pretty cool. Same for bibliographic coupling.
So, some conclusions about this.
MesoScale is second scale of analysis.
So, some publications are influential to archaeologists and also in other disciplines.
The large number of publications influenced on physics and social network analysis
were not influential to archaeologists,
and that it has been identified through those community detection algorithms,
identified a lot of things that archaeologists were never aware of or never decided to use,
which is not a problem. I mean, it's normal that you would only use those things
that are very popular and have proven their value in other disciplines.
It's very normal, but at some point, someone has to come along and say,
well, let's take a bit of a wider scope, look at things that might also be useful.
I think this approach allows me to identify those things.
And then thirdly, through those disciplinary communities, we can do something like that.
But it's still an archaeological network because we're still focusing on the direct publications,
the direct citations by those archaeologists.
So, let's take a bit of a wider scope and look at a network of 33,000 publications
connected by 42,000 citations.
Now, obviously, this is the bias of the Web of Knowledge selection criteria are the highest here.
And we get a lot of noise, basically.
It's extremely incomplete.
But to assess the usefulness of Web of Knowledge citation database for archaeology,
I think this network is particularly interesting.
One measure that was interesting is identifying hubs and authorities.
And a hub is a paper that cites a lot of other papers and that cites a lot of good authorities in particular.
And an authority is a paper that is cited by a lot of other papers and is cited by a lot of good hubs in particular.
Very simple, but very powerful measure because we get something like this.
Now, the hubs are all physicists in this case,
publishing reviews in prominent physics journals, as you do.
The authorities are, instead, relatively old publications,
but we're all fundamental publications in the emergence of new methods, approaches, theories,
mainly advocated by physicists like the self-organized criticality or fractals while Mandel brought, for example.
This is quite interesting.
They are, they really are authorities.
They have been influential.
Again, let's look at the input domain measure.
And we get something like this, again, stressing the older publications that were very influential to physicists,
not necessarily to archaeologists.
And it's really where the influence, especially for the small world network model in this case, for example,
should be traced back to, in this case, Stanley Milgram's initial experiments with passing out letters
and coining this kind of phrase.
We live in a kind of small world.
It can be connected together by at least six steps, or a maximum of six steps, to be honest.
So, some conclusion on this level.
There's a clear bias within the citation dataset towards physics in this case,
but in general, that can be generalized to, say, disciplines that just have a more formal way of publishing,
that is enforced or not, and that focuses on those journals that web of knowledge just traditionally has in their databases.
Archaeology is not part of that.
Older publications influential to physicists are very prominent,
and the archaeologists working in a physics tradition stand out most.
And that is a conclusion drawn from, again, looking at the entire mass and zooming in on this,
although I can't illustrate that in this presentation.
Now, some final conclusions.
So, what I was initially interested in is seeing how archaeologists have used formal network methods,
where they were influenced, by which publications they were influenced,
how they decide to adopt that, and how they decided to adapt that,
and how the use of that network method evolved in the archaeological discipline.
To do that, I did a close reading.
There were a couple of conclusions that were drawn for that,
and a couple of those were validated by the citation network approach,
the ones that I explain here, for example.
But on the other hand, there were some very strong limitations to the citation approach.
And one thing I really wanted to stress is just something I was thinking of today, to be honest,
is, actually, it's a very bad case study.
The use of methods, of network methods, within archaeology
is really not going to be usefully explored through citation network analysis,
because it does not reflect how those authors and those publications stand
within their own disciplines and sub-disciplines.
To give the example of Pacific archaeology again,
I did a couple of interviews with people who used this for Pacific archaeology in the 1970s,
and have been reusing it recently.
They say that their methods were not influential
within this group of archaeologists using network methods,
but not even within the Pacific archaeology literature, for example.
And that is not reflected here in any way.
And to really see the influence of how these methods were used,
and evolved within their discipline, and why they actually were disused for 20 years,
cannot be explored by this.
On the other hand, it was a very interesting exercise
that really stressed the inadequacy of citation databases that are freely available to academics.
They just basically exclude a lot of data, which is very problematic for archaeology.
It works very well for me on a local level.
It becomes increasingly problematic when we're looking at a very large scale.
And then the most useful for tracing main paths of influence.
So it was very, very easy to see those obvious things that I already knew.
The very popular techniques, textbooks, and models by physicists and social network analysts just jumped out.
But a close reading explains how and why those publications were influential.
So it's an interesting, it was a very interesting exercise.
Now, it doesn't stop here.
There's a lot of archaeologists interested in networks.
If you are interested in networks in some way, we're organizing conferences, we've got discussion lists.
Just please talk to me. And thank you so much for your attention.