music – Michele Coscia

One of the blessings of being a digital humanist is being able to study something as inherently interesting as music. A while ago, I had fun trying to figure out which band was the most important in the Italian music scene. That was cool, but it left open a question, which I want to address today: are the patterns that I’m seeing specific to Italian music, or do they hold in another country?

To answer this question I needed to collect data about at least another country. Lisandro Marco Benetti and Daniel Fejerskov-Quist helped me compile a new dataset about Danish music. The result was the paper we co-authored: “Comparing the Italian and Danish Music Industries With Network Analysis,” which appeared in the journal Complexity earlier this month.

We applied the same methodology as in my previous paper to the bands releasing records in Denmark, to create a network that was directly comparable with the Italian one: bands connected to each other if they share a significant number of artists. Then, we could compare the two to figure out what stayed the same, what was different, and more.

Nodes are bands, connected by artist sharing. Node color is the average year of the band’s releases. Node size is centrality. Edge color is significance. Denmark on the left, Italy on the right.

The overall structure remained largely the same, with clear clustering by music genre and a temporal gradient, as you can see from the picture above. Sure, Denmark is smaller, has fewer bands, and a shorter time span, but overall the important structural properties of the networks are the same.

One new analysis we introduced in this paper is label nestedness. Record labels release music, so it is interesting to understand how specialized they are. When we sort labels by how much music they release, we see a nested pattern: small labels tend to focus on popular genres that are published by almost every label, while the many small specific genres are only published by the largest labels.

The dispersion of bands in the network (y axis) over time. The color background compares the observed value with what we’d expect given the number of records published. Green shows more dispersion than expected, red is less dispersion than expected (white matches expectation). Denmark on the left, Italy on the right.

Things start getting interesting when we look at the differences. For instance, by using network variance, we can quantify how much a year was innovative, by looking at how dispersed across the network the bands releasing records that year are (meaning the releases come from unrelated bands). We can control the amount of music produced, so that we know we’re not just looking at a lot of music, but specifically at a diverse body of music. The figure above shows that Denmark had its peak diversification during the 1980s and 1990s, while Italy was innovative earlier, in the 1970-1980 period.

Other differences include how the two music industries relate to different genres. Some genres show a low degree of specialization — meaning many unrelated bands have played them — in either Italy or Denmark, but not both. For instance, a diverse set of bands had tried to make beat music in Italy at its heyday, but not in Denmark. On the other hand, pop rock crops up across many unrelated Danish bands, not systematically, but this is not true in Italy. Various metal genres are also quite central in Denmark, but marginal in Italy.

The merged Danish-Italian music industry network. Nodes are still bands connected by artist sharing, with their size proportional to their centrality. Blue nodes for Italy, red nodes for Denmark.

The cool thing about this exercise is that, by using a consistent convention in making the dataset, the two networks can be merged. Artists who contributed to both Italian and Danish bands can create links across the countries. The global structure is what you see in the above figure. This significantly changes the centrality calculation. Rather than looking at the most important bands within a country, we are now looking at those bands that tie together two different countries.

Here are three examples:

Temperance: this Italian melodic power metal band is central because their records are produced by Jacob Hansen, a key figure in Danish rock / heavy metal;
Robertino: an Italian singer who was noticed at age 10 by Danish TV producer Volmer Sørensen, who launched his career;
Alex Puddu: an Italian funk artist born in Rome, but who relocated in Copenhagen at age 20*.

This has been fun, but, if you know me, you know that I don’t know when it’s time to quit. Of course the next step is to crank this up to eleven. Are the commonalities we see between Denmark and Italy just a coincidence? What if we add a third country? What if we add thirty? You’ll have to stay tuned, as I’m working on a paper to conclude this trilogy.

* To make the figure above, we used the country of origin to decide the node’s color. Alex Puddu was the one case we couldn’t quite figure out. So Lisandro and Daniel resolved to do the only logical thing, and just wrote Alex himself. He was very kind in answering, with details that would likely be appreciated by my friend Max Schich, as in the end even Alex couldn’t call himself either Italian or Danish. In the end, I flipped a coin 🙂

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:

The temporal dimension: nodes are bands, connected by significant sharing of artists. The node color is the average year of a released record from the band.

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:

Network variance (y axis) over the years (x axis). High values in green show times of high dynamism, low values in red show times of structural concentration.

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.