Digital humanities have seen fast growth in the last ten years. Digital humanities scholars of all fields extract data from their object of study and apply computational methods to answer research questions and to gain new insights. Much of the data collected from, e.g., books, historical texts, publications, dialogs, speeches, archaeological databases, and art pieces can be modeled as networks. Existing network analysis and visualization techniques have already proven themselves useful in analyzing these data and providing new discoveries.

The central goal of the network visualization field is the development of techniques and algorithms for effective network visualization. While network visualization strongly impacts a variety of different major research areas (incl. social science, bioinformatics, neuroscience, electronics, software engineering, business informatics, etc.), the humanities fields use only a tiny portion of the existing network visualization methods. On the other hand, network visualization scholars have not made an extensive attempt to develop and adapt techniques to be used by humanities researchers. Besides the general force-directed algorithm, there is a very limited number of other types of network visualization techniques that have been successfully applied in the humanities. This is partially due to the fact that existing network visualization tools fail to provide support to the humanities scholars, for a number of reasons:

1. They are designed for very different kinds of data sets to those typically occurring in the humanities, which are often incomplete and come from various sources; i.e., the traditional social network analysis metrics, based on which visualizations are often constructed, are of little value.
2. Typical network visualization techniques are designed to work on a scale too coarse to be used to display humanities data. In particular, questions in humanities are typically more granular than in most social sciences, and therefore require analyses finer than those currently afforded by most visualization software.
3. Humanities scholars often “think through data.” In other words, they need a visualization that is interactive, and not only for exploring results. Current visualization tools provide limited possibilities for interactivity. Moreover, interaction requirements heavily depend on the application and use case.
4. Visualization requirements in humanities fields are very specific and can be formulated by only considering particular applications and use cases. Such specific requirements are rarely taken into consideration by visualization tools. However, recent practice has shown that adaptations of existing visualization methods may prove themselves very valuable for humanities scholars.

In order to promote the development of newly tailored and effective visualization solutions as well as in order to support the adaptation of already existing visualization techniques and their integration into humanities scholars’ research routine, this Dagstuhl Seminar will bring together researchers from network visualization and digital humanities communities for the first time. Through several stimulating talks and brainstorming sessions the researchers will inform each other about current challenges in network visualization in the humanities and about existing visualization methods. Afterwards, working in smaller groups, the researchers will aim for a research agenda for network visualization challenges in the humanities.

The main goals of the seminar are:

• Information diffusion: network visualization experts will inform about existing methods and algorithmic techniques for network visualization; humanities scholars will inform about data sources, application backgrounds, questions, potential use cases, etc. in the humanities.
• A research agenda for “Network Visualization in Digital Humanities” listing applications, corresponding data and data sources, use cases, visualization and interaction requirements, possible existing visualization solutions, new visualization challenges, and necessary adaptations.
• The creation of interdisciplinary research teams that address specific scientific challenges from the agenda as well as the stimulation of collaborations and research projects.

Creative Commons BY 3.0 DE

Katy Börner, Dan Edelstein, Tamara Mchedlidze, Gerik Scheuermann, and Raymond G. Siemens

• Gregor Betz (KIT - Karlsruher Institut für Technologie, DE) [dblp]
• Katy Börner (Indiana University - Bloomington, US) [dblp]
• Ulrik Brandes (ETH Zürich, CH) [dblp]
• Nicole Coleman (Stanford University, US) [dblp]
• Melanie Conroy (University of Memphis, US)
• Marten Düring (University of Luxembourg, LU) [dblp]
• Tim Dwyer (Monash University - Caulfield, AU) [dblp]
• Oyvind Eide (Universität Köln, DE) [dblp]
• Kimmo Elo (University of Turku, FI)
• Sara Irina Fabrikant (Universität Zürich, CH) [dblp]
• Christina Gillmann (TU Kaiserslautern, DE) [dblp]
• Hans Hagen (TU Kaiserslautern, DE) [dblp]
• Francis Harvey (Leibniz Institut für Länderkunde - Leipzig, DE) [dblp]
• Gerhard Heyer (Universität Leipzig, DE) [dblp]
• Stefan Jänicke (Universität Leipzig, DE) [dblp]
• Fotis Jannidis (Universität Würzburg, DE) [dblp]
• Andreas Kerren (Linnaeus University - Växjö, SE) [dblp]
• Stephen Kobourov (University of Arizona - Tucson, US) [dblp]
• Tamara Mchedlidze (KIT - Karlsruher Institut für Technologie, DE) [dblp]
• Martin Nöllenburg (TU Wien, AT) [dblp]
• Malte Rehbein (Universität Passau, DE) [dblp]
• Gerik Scheuermann (Universität Leipzig, DE) [dblp]
• Antonios Symvonis (National TU - Athens, GR) [dblp]
• Timothy Tangherlini (University of California at Los Angeles, US) [dblp]
• Christopher Warren (Carnegie Mellon University - Pittsburgh, US) [dblp]
• Scott Weingart (Carnegie Mellon University - Pittsburgh, US) [dblp]
• Florian Windhager (Donau-Universität Krems, AT) [dblp]