Journal of the Royal Society Interface publishes work spanning multiple disciplines and can bring together topics that may not have been considered together before. In a review article published in 2020, network science is applied to the concept of beauty. We spoke to the author, Professor Matjaž Perc at the University of Maribor, about the background to the review and the potential for this research area.
1. Can you briefly introduce your review and tell us what it's about?
Beauty is subjective, and as such it of course cannot be defined in absolute terms. But we all know or feel when something is beautiful to us personally. And in such instances, it turns out, methods of physics and network science can be used to quantify and help us better understand what it is that evokes that pleasant feeling, be it when reading a book or when looking at a painting. Indeed, recent large-scale explorations of digital data have lifted the veil on many aspects of our artistic expressions that would remain forever hidden in smaller samples. In our review, we cover culinary, visual, musical, and literary arts, and we also touch upon cultural history and culturomics, as well as on the connections between physics and the social sciences in general. Our review shows that the synergies between these fields yield highly entertaining results that can often be enjoyed by layman and experts alike.
2. Why did you decide to focus on this topic?
The impetuous was reviewing "Collective Motion of Humans in Mosh and Circle Pits at Heavy Metal Concerts" for Physical Review Letters back in 2013. It was the first time that I remember coming across research that somehow combined art and physics, and I was fascinated by it. Then in 2018 we published, the paper "History of art paintings through the lens of entropy and complexity" in PNAS with Higor Y. D. Sigaki and Haroldo V. Ribeiro. I then ended up presenting it at several conferences, always to a warm reception and encouragement to continue along these lines. Soon thereafter I started working on the review, hoping it would be the best way to learn as much about this field as possible, as well as providing the community with an informative and useful account of research at this interface.
3. How was your experience publishing with J. R. Soc. Interface?
It was absolutely wonderful. I have published reviews with J. R. Soc. Interface before, for example "The Matthew effect in empirical data" and "Evolutionary dynamics of group interactions on structured populations", and I was always so pleased with the way these works have been widely read and cited. So when I begun writing this review, I knew from the start that J. R. Soc. Interface would be my first choice. I was also very happy to receive favorable reports and to succeed in getting this paper accepted in such a fine journal. I can only thank everyone at J. R. Soc. Interface for the great experience.
4. What do you think the future holds for further research into the relationship between network science and the arts?
The popularity of this line of research has been growing steadily in recent years, and I believe this will continue to be the case as more and more large-scale digitalization efforts provide unprecedented access to various art forms. Artificial intelligence also seems poised to play an ever more important role in bridging the gaps between human creativity and the digital world. Thankfully, when we think about artificial intelligence evaluating and contributing to art, there seem to be less dilemmas to consider than when the same technology is applied to medicine or decision making. The future will very likely also see this type of research implemented in various applications, ranging from improved recommendations to plagiarism detection.
Image credits: Vincent van Gogh - bgEuwDxel93-Pg at Google Arts & Culture, Public Domain, https://commons.wikimedia.org/w/index.php?curid=22493244
To take a look at more cutting-edge work at the boundary of biology and physics, and to find out how to submit, check out https://royalsocietypublishing.org/journal/rsif.