https://www.michelecoscia.com/?p=2372 Michele Coscia Connecting Humanities * Home * Teaching + Project Ideas * Publications * Code + DEMON + Multidimensional Network Analysis + Network Backboning + UBIK + Leader Detect + Node Vector Distance + Noise Corrected Network Sampling + YCN-RW: Bipartite Projections + Arborescence Hierarchicalness + Health Crawler + Benchmark for Network Sampling + Node Attribute Clustering + Multiplex Link Prediction (MAGMA) * Datasets + Supermarket Data + Social and Mobility Networks of Colombia + Mexico Drug Traffic Activities + Supplementary Data for Business Travel Project + Economic Inclusion and Human Mobility in Bogota + Community Discovery Network + Italian Music Bands + The Product Space + Memetics + Job Flows * CV * Contact * Reproducibility + Intolerance is Attractive + Quantifying Polarization on a Network Using Effective Resistance + Sport Predictability + Pearson Correlations on Networks: Addendum + Unequal Entry Requirement in Wikipedia Based on Gender 26 September 2024 ~ 0 Comments Italian Music through the Lens of Complex Networks Digital Humanities 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. [image]Image credit: bresciaoggi.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. [image-1]Image credit: wikipedia.org 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: [image-2-1024x852]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: [image-4]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: [image-3-1024x852] 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. [image-5-1024x797]Image credit: wikipedia.org Tags: complex networks, cultural data analytics, digital humanities, music, node attribute analysis, node vector distance, social networks Leave a Reply Click here to cancel reply. [ ] Name (required) [ ] Mail (will not be published) (required) [ ] Website [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [Submit Comment] Search for: [ ] [Search] People I find interesting * Maximilian Schich * Sune Lehmann * Cesar A. 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