Networks are used to model and represent a large variety of data in many application areas from life sciences to social sciences. Visual network analysis is a crucial tool to improve the understanding of data sets and processes over many levels of complexity, e.g., semantic, spatial and temporal granularity. While there is a great deal of work on the algorithmic aspects of network visualization and the computational complexity of the underlying problems, the role and limits of human perception are rarely explicitly investigated and taken into account when designing network visualizations.

To address this issue, this Dagstuhl Seminar is meant to increase awareness in the network visualization community of the need for more extensive theoretical and empirical understanding of how people perceive and make sense of network visualizations and the significant potential for improving current solutions when perception-based strategies are employed. Likewise, the seminar is supposed to increase awareness in the perception community that challenges in network research can drive new questions for perception research, for example, in identifying features and patterns in large, often time-varying networks. We would like to bring together researchers in the communities to initiate a dialogue, foster exchange, discuss the state of the art at this intersection and within the respective fields, identify promising research questions and directions, and start working on selected problems.

Perception can play an important role in nearly all aspects of network visualization and we aim to cover diverse aspects in the topics that will be investigated during the seminar, with the following short list serving as a starting point for further discussions:

• Fundamentals of perception in relation to network visualization: Basic questions about how humans read network visualizations in the context of specific network characteristics and tasks are not yet well understood. We would like to investigate some of these questions, including: What are main features that humans recognize and memorize from different network representations? How well can they be distinguished and how sensitive are people to changes in these features? What are the main features that support orientation and navigation in large networks? What are the relationships between insight-generation, perception and interaction in interactive exploration scenarios?
• Quality metrics and layout styles: Many quality metrics and optimization goals for different layout styles have been proposed (e.g., number of crossings, stress, number of bends). We want to investigate whether these metrics and goals are motivated or justified by modern theories of perception, and whether empirical evidence exists to support them. Can the current knowledge on perception explain why certain approaches work better than others?
• Experiment design: Investigating the above questions requires new experimental paradigms that consider the complex relationship between elements in network visualizations (e.g., nodes and edges) and the insights that people develop with such visualizations. Experimental methods must both investigate perceptual aspects of network visualization and provide meaningful evaluations of new metrics and approaches.
• Guidelines: Network visualization covers more than algorithmic aspects. Is it possible to develop guidelines that help steer the complex design process using perceptual principles?

Creative Commons BY 4.0

Karsten Klein, Stephen G. Kobourov, Bernice E. Rogowitz, and Danielle Szafir

• Michael Aichem (Universität Konstanz, DE)
• Daniel Archambault (Swansea University, GB) [dblp]
• Cindy Xiong Bearfield (University of Massachusetts - Amherst, US)
• Annika Bonerath (Universität Bonn, DE)
• Rita Borgo (King's College London, GB) [dblp]
• Claus-Christian Carbon (Universität Bamberg, DE) [dblp]
• Tim Dwyer (Monash University - Clayton, AU) [dblp]
• Peter Eades (The University of Sydney, AU) [dblp]
• Henry Förster (Universität Tübingen, DE) [dblp]
• Mohammad Ghoniem (Luxembourg Inst. of Science & Technology, LU) [dblp]
• Carsten Görg (University of Colorado - Aurora, US) [dblp]
• Seok-Hee Hong (The University of Sydney, AU) [dblp]
• Christophe Hurter (ENAC - Toulouse, FR) [dblp]
• Andreas Kerren (Linköping University, SE) [dblp]
• Karsten Klein (Universität Konstanz, DE) [dblp]
• Felix Klesen (Universität Würzburg, DE)
• Stephen Kobourov (University of Arizona - Tucson, US) [dblp]
• Oliver Kohlbacher (Universität Tübingen, DE) [dblp]
• Giuseppe Liotta (University of Perugia, IT) [dblp]
• Mauro Martino (MIT-IBM Watson AI Lab - Cambridge, US) [dblp]
• Tamara Mchedlidze (Utrecht University, NL) [dblp]
• Natalia Melnik (Otto-von-Guericke-Universität Magdeburg, DE)
• Silvia Miksch (TU Wien, AT) [dblp]
• Jacob Miller (University of Arizona - Tucson, US)
• Kazuo Misue (University of Tsukuba, JP)
• Fabrizio Montecchiani (University of Perugia, IT) [dblp]
• Carolina Nobre (University of Toronto, CA)
• Martin Nöllenburg (TU Wien, AT) [dblp]
• Alexander Pastukhov (Universität Bamberg, DE) [dblp]
• Maurizio Patrignani (University of Rome III, IT) [dblp]
• Helen C. Purchase (Monash University - Clayton, AU) [dblp]
• Marius Raab (Universität Bamberg, DE) [dblp]
• Bernice E. Rogowitz (Visual Perspectives - New York, US) [dblp]
• Paul Rosen (University of Utah - Salt Lake City, US) [dblp]
• Falk Schreiber (Universität Konstanz, DE) [dblp]
• Danielle Szafir (University of North Carolina at Chapel Hill, US) [dblp]
• Alessandra Tappini (University of Perugia, IT)
• Alexandru C. Telea (Utrecht University, NL) [dblp]
• Markus Wallinger (TU Wien, AT)
• Hsiang-Yun Wu (FH - St. Pölten , AT) [dblp]