In recent years, attention has been drawn in both the academic and popular press to the ubiquity of networks in everyday life, from communications networks to investment networks to power transmission networks to social networks. As a result of this increasing awareness, the study of the different types of networks that link us together, and the analysis of the structure of those networks has risen to greater and greater prominence not only in the mathematical and social sciences but also in the Humanities. Despite this increasing awareness of the importance of networks for theoretical advances in the Humanities, there is a considerable gap between recognizing in the broadest strokes the existence of these complex, dynamic systems and the very hard work of the consistent application of rigorous theoretically sound methods to the study of networks. Computational tools for the discovery and analysis of networks offer the promise of bridging this gap; unfortunately, many of these tools are as complex to work with as the underlying data itself. A main goal of this institute is to teach Humanities scholars some of the most accessible of these techniques.
In broadest terms, the topics to be addressed in the Institute are: (a) the science of networks and networks in Humanistic inquiry (b) preparing and cleaning Humanities data for network analysis (c) internal networks in Humanistic data: networks of characters, networks of texts, networks of language (d) external networks in Humanistic data: networks of influence, networks of production, networks of reception.
The institute, housed at UCLA, features lectures and tutorials from some of the leading scholars in Network Analysis and Visualization. The schedule will also allow participants an adequate opportunity to interact, to experiment and to learn from the institute faculty. The majority of the faculty come from the Applied Math and Computer Science community who have an interest in developing and applying tools for the type of corpora with which Humanities scholars typically work. There is significant time set aside during the institute for two types of important activity: (1) independent and group learning/experimentation with software on test datasets, so that lessons learned are not purely theoretical, but are have an applied component to them as well (2) structured free-time for developing collaborative ideas.
Zoe Borovsky (University of California, Los Angeles (UCLA))
Gregory Crane (Tufts University)
Tina Eliassi-Rad (Rutgers University)
Mark Green (University of California, Los Angeles (UCLA))
Peter Jones (Yale University)
Lewis Lancaster (University of California, Berkeley (UC Berkeley))
Timothy Tangherlini (University of California, Los Angeles (UCLA))