Warning: Undefined array key "file" in /home/amasonsi/hh2022.amason.sites.carleton.edu/wp-includes/media.php on line 1686
Abstraction is a modern art type that boomed in the early 1900s, sweeping across media forms like painting, photography, and sculpture. Pioneers of the field like Pablo Picasso or Francis Picabia, deeply connected to their communities as artists, held tremendous influence in the early stages of its development. In 2018, the MoMa began using machine learning in collaboration with Google Arts & Culture Lab to identify artworks in installation photos and digitize their exhibit on the development of abstraction. The project is visible on the MoMa website, showcasing the deeply interconnected network of artists that pioneered abstraction’s early development.
“The exhibition brings together many of the most influential works in abstraction’s early history and covers a wide range of artistic production, including paintings, drawings, books, sculptures, films, photographs, sound poems, atonal music, and non-narrative dance, to draw a cross-media portrait of these watershed years.”MoMa: Inventing Abstraction 1910-1925
Each node is an artist that played a part in the development of abstraction that took place between 1910-1925: 85 artists are featured in the exhibit. Each documented relationship between these artists during that period is represented by a vector (red connecting line); artists with over 24 connections shown are highlighted in red. Clicking on and visiting an artist’s page will isolate their connections (as well as expand relevant information about their life and showcase notable works).
As abstraction, like other artistic movements, was developed by a community of interacting minds rather than a linear chain of influence, a different model like this is necessary to better understand the development and pivotal moments in its history. One artist of the genre, as evidenced in this model, is unlikely to have had one influence on their work, or one significant interaction at a single time. Mapping in this way is a much more effective means of understanding the community at the time, as it more accurately portrays the process of artistic development.
I agree. No graph format is better at communicating the idea of community than network graphs.