Michele Coscia

The past fascinates us. I often fantasize about how our ancestors lived: what did they do? How did they relate to one another? Were their social networks similar to ours? Archaeology is the way we try to answer these questions if we do not have written records. The main problem is that archaeology finds stuff — material culture. Social relationships can’t be dug up from the mud.

Together with Camilla Mazzucato and a team of archeologists and biologists, we decided to investigate whether we could actually infer the social relationships from the material culture we found. The result was the paper “‘A Network of Mutualities of Being’: Socio-material Archaeological Networks and Biological Ties at Çatalhöyük“, which appeared in January in the Journal of Archaeological Method and Theory.

The paper focuses on the site of Çatalhöyük which was inhabited in the Neolithic for several millennia. Çatalhöyük is the ideal place to draw connections between found material culture and interpersonal relationships because of some peculiar habits in the culture which inhabited the site. Upon the construction of a new building in the settlement, in fact, the habit was to bury the dead in the foundations (humans are weird).

Having lots of dead bodies is fantastic (all of a sudden I sound like Hannibal Lecter) because now in the buildings we can find material culture and human remains, both of which had a connection with the building itself. From the human remains we can infer kinship relationships between buildings, because people related to each other are buried in them.

This is where the team of biologists come in. They analyze the DNA of the human remains to establish which pairs of individuals have a first, second, or third degree relationship. Once we have both material culture and kinship relationships between buildings we can start asking ourselves whether the two correlate or not.

One way to do it is by using network variance. We can connect buildings — the nodes in the network — if they share significant amounts of material culture. Then, for a given kinship group — a set of people with at least second or third degree relationships –, we can create a numeric node attribute: how many individuals from that family were buried in each building.

The network variance of this attribute tells us how dispersed the family is in the material culture landscape. By comparing this with a null family — a family that buries individuals at random — we can know whether the real family tended to be more concentrated in the material culture space than expected, a sign that material culture and kinship correlate. Which is what we observe!

There are a number of possible alternative explanations, of course. One we checked for is geotemporal proximity. Buildings nearby each other and built around the same time also share more material culture and related individuals. So we control for geotemporal proximity and we see that the effect is still significant. In other words: it is more likely for related individuals to be buried in buildings with similar material culture, regardless of when and where these buildings were built. We also check the robustness of our results by slightly changing the way we build our networks, and the way we analyze the DNA remains — the result is still there.

Our result suggests a nice consequence: it is justified to say that sites containing the same material culture hosted individuals that were related to each other. Found material culture can tell us something interesting and meaningful about the shape and dynamics of past social networks.

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Last year I was talking with a non-Italian, trying to convey to them how nearly the entirety of contemporary Italian music rests on the shoulders of Gianni Maroccolo — and the parts that don’t, should. In an attempt to find a way out of that conversation, they casually asked “wouldn’t it be cool to map out who collaborated with whom, to see whether it is true that Maroccolo is the Italian music Messiah?” That was very successful of them, because they triggered my network scientist brain: I stopped talking, and started thinking about a paper on mapping Italian music as a network and analyzing it.

One year later, the paper is published: “Node attribute analysis for cultural data analytics: a case study on Italian XX–XXI century music,” which appeared earlier this month on the journal Applied Network Science.

I spent the best part of last year crawling the Wikipedia and Discogs pages of almost 2,500 Italian bands. I recorded, for each album they released, the lineup of the song players and producers. The result was a bipartite network, connecting artists to the bands they contributed to. I tried to have a broad temporal span, starting from the 1902 of Enrico Caruso — who can be considered the first Italian musician of note (hehe) releasing actual records — until a few of the 2024 records that were coming out as I was building the network — so the last couple of years’ coverage is spotty at best.

Then I could make two projections of this network. In the first, I connected bands together if they shared a statistically significant number of players over the years. I used my noise corrected backboning here, to account for potential missing data and spurious links.

This is a fascinating structure. It is dominated by temporal proximity, as one would expect — it’s difficult to share players if the bands existed a century apart. This makes a neat left-to-right gradient timeline on the network, which can be exploited to find eras in Italian music production by using my node attribute distance measure:

You can check the paper for the eras I found. By using network variance you can also figure out which years were the most dynamic, in terms of how structurally different the bands releasing music in those years were:

Here we discover that the most dynamic years in Italian music history were from the last half of the 1960s until the first half of the 1980s.

There is another force shaping this network: genre. The big three — pop, rock, electronic — create clear genre areas, with the smaller hip hop living at the intersection of them:

Just like with time, you can use the genre node attributes distances to find a genre clusters, through the lens of how they’re used in Italian music.

What about Maroccolo? To investigate his position, we need to look at the second projection of the artist-band bipartite network: the one where we connect artists because they play in the same bands. Unfortunately, it turns out that Maroccolo is not in the top ten most central nodes in this network. I checked the degree, closeness, and betweenness centralities. The only artist who was present in all three top ten rankings was Paolo Fresu, to whom I will hand over the crown of King of Italian Music.

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