Two weeks ago the Nobel prize in physics was awarded to Hopfield and Hinton for their research on artificial neural networks. This caused quite some uproar, especially by many of our computer science and physics colleagues. As original-physicists-turned-data-scientists-dabbling-in-AI, who have done data-driven Science of Science research exactly on the crucial role of Hopfield and Hinton’s papers in physics, we penned a comment pointing to our clarifying research which was now published as a correspondence in Nature:

Was the Nobel prize for physics? Yes — not that it matters, by M. Szell, Y. Ma, and R. Sinatra

Here the entire correspondence:

The award of the 2024 Nobel Prize in Physics to John Hopfield and Geoffrey Hinton for their groundbreaking research on artificial neural networks (Nature 634, 523–524; 2024) has caused consternation in some quarters. Surely this is computer science, not physics?

Existing data can help to inform this debate. Almost a decade ago, two of us (M.S. and R.S.) co-authored an analysis of referencing and citation patterns that explicitly placed Hopfield’s seminal 1982 paper on neural networks among 3.2 million interdisciplinary papers in non-physics journals that were “indistinguishable from papers published in physics journals”. Six other physics Nobel-winning papers were also in this set (R. Sinatra et al. Nature Phys. 11, 791–796; 2015).

The physics Nobel prize has until recently rewarded conventional ‘core’ physics research, even though Hopfield’s and Hinton’s papers were ripe for recognition (M. Szell et al. Nature Phys. 14, 1075–1078; 2018). We hope that this year’s prize will expedite the breakdown of silos that obstruct thinking across disciplines. Clinging to the idea of research fields as fixed territories is at best small-minded, and at worst harmful, when it comes to solving global challenges such as climate change.

Our original version – before editorial changes – provides a slightly different angle and an instructive figure (that was cut for publication):

Our newest faculty hire Jonas L. Juul is already making a splash. He published a big multi-author paper in Nature Communications: High-resolution epidemiological landscape from ~290,000 SARS-CoV-2 genomes from Denmark, by M.P. Khurana et al

We are happy that with Jonas, who was part of the Statens Serum Institut’s expert group on mathematical modeling of COVID-19 during the reopening of Denmark in the spring and summer of 2020, we have gained a solid footing in medical applications of data/network science.

We examined the drivers of molecular evolution and spread of 291,791 SARS-CoV-2 genomes from Denmark in 2021. With a sequencing rate consistently exceeding 60%, and up to 80% of PCR-positive samples between March and November, the viral genome set is broadly whole-epidemic representative. We identify a consistent rise in viral diversity over time, with notable spikes upon the importation of novel variants (e.g., Delta and Omicron). By linking genomic data with rich individual-level demographic data from national registers, we find that individuals aged  < 15 and  > 75 years had a lower contribution to molecular change (i.e., branch lengths) compared to other age groups, but similar molecular evolutionary rates, suggesting a lower likelihood of introducing novel variants. Similarly, we find greater molecular change among vaccinated individuals, suggestive of immune evasion. We also observe evidence of transmission in rural areas to follow predictable diffusion processes. Conversely, urban areas are expectedly more complex due to their high mobility, emphasising the role of population structure in driving virus spread. Our analyses highlight the added value of integrating genomic data with detailed demographic and spatial information, particularly in the absence of structured infection surveys.

A new NERDS authored paper is out in Applied Network Science: Node attribute analysis for cultural data analytics: a case study on Italian XX–XXI century music, by M. Coscia

We use the Italian music record industry from 1902 to 2024 as a case study. In this scenario, a possible research objective could be to discuss the relationships between different music genres as they are performed by different bands. Estimating genre similarity by counting the number of records each band published performing a given genre is not enough, because it assumes bands operate independently from each other. In reality, bands share members and have complex relationships. These relationships cannot be automatically learned, both because we miss the data behind their creation, but also because they are established in a serendipitous way between artists, without following consistent patterns. However, we can be map them in a complex network. We can then use the counts of band records with a given genre as a node attribute in a band network. In this paper we show how recently developed techniques for node attribute analysis are a natural choice to analyze such attributes. Alternative network analysis techniques focus on analyzing nodes, rather than node attributes, ending up either being inapplicable in this scenario, or requiring the creation of more complex n-partite high order structures that can result less intuitive. By using node attribute analysis techniques, we show that we are able to describe which music genres concentrate or spread out in this network, which time periods show a balance of exploration-versus-exploitation, which Italian regions correlate more with which music genres, and a new approach to classify clusters of coherent music genres or eras of activity by the distance on this network between genres or years.