The project that I will be working on over the course of Rebuilding the Portfolio is Mapping Paris: Social and Artistic Networks, 1855-1889. Here is an abstract of the project:
Mapping Paris, a project in its start-up phase, charts and analyzes nineteenth-century social networks in order to map the artistic collaborations taking place in Paris between the Universal Expositions of 1855 and 1889. In these years, Paris saw the shifting role of the Salon, the exhibitions that sprung up in counter-dialogue, and the expansion of galleries. It is during this period that Paris claimed its role as the center of the Western art world, and as a result, scores of international artists descended upon to the city to live and work. Mapping Paris plots the social relationships established in Paris during this time, a cosmopolitan mélange of aesthetic exchange and artistic cooperation.
Finding its place between collaborative and crowd-sourced inquiry, Mapping Paris provides a space on the internet for refereed individuals to input data about artists’ time in Paris, whether they were Parisian pure-sang, ex-pats or visitors passing through the city. In doing so, it allows scholars to view the data in novel ways, to foster considerations of aesthetic dialogue through crossed-paths, acquaintances, friendships, conversations and collaborations in the social condenser of Paris.
The networks are mapped through primary source documents, including letters, memoirs, and exhibition records. This practice examines the nodes (i.e. the individuals) and the links between them. Mapping Paris takes into account the possibility of other connectors, alongside the more traditional hubs of nineteenth-century art historical discourse, such as the Impressionists. It provides the opportunity to engage and reconsider the concepts of center and periphery in Paris itself, renegotiating the international nature of this cosmopolitan city.
Mapping Paris evolved from my dissertation work on the social networks of Degas and contemporaneous Italian artists and their resultant artistic repercussions. During my research for the project, I realized the difficulty in keeping track of and analyzing social networks without a layered, visual means by which to examine then. Instead, I kept lists of social interactions, letters, and other primary sources of known contact between the individuals. I spent a lot of time and space establishing a credible, text-based social network, upon which I then began to investigate the resultant implications in the realm of artistic process and exchange. Upon reflection, I have realized that it will prove much more useful and efficient to visually map out these social networks, and expand them beyond the realm of just Degas and the Italians, opening up consideration of the larger artistic networks of Paris during this time period.
What Mapping Paris brings to the equation is the ability to quantitatively visualize these networks, and in doing so, ask ‘bigger’ research questions – questions that engage with the larger whole of the social network, and not just case studies of nodes. A sample, non-directional social network can be seen here, a mapping of the networks of Degas, his Italian contemporaries and their circles [click on image to enlarge]:
Each node is connected by an edge and the references for each edge have been bibliographically cited with notation. Another direction this map could take is to add weights (i.e. multiple references). In this sample, each node is only connected via one, unweighted edge, mapped using a Yifan Hu layout. The map allows the viewer to get a sense of the social relations between groups of individuals, allowing for an understanding of those nodes that function as expected hubs (Degas) and connectors (e.g. Desboutin, De Nittis, Martelli, etc.). The source information for this map is found in the primary sources referenced in a chapter of my dissertation – and thus the map itself is admittedly biased, showing Degas as the major social condenser of the network (which was, after all, my thesis). Such an example points out the need for other data inputs, to consistently move towards more data and thus less bias within the sample.