Networks seem to be everywhere: From social media, biology, historical research, family trees, software engineering, or transportation up to infection chains. Complex phenomena are often modeled as networks to study their key characteristics and communicate their developments. Networks usually consist of nodes (e.g., persons) and edges (e.g., relations between persons) connecting nodes and may furthermore include several additional dimensions such as time, locations, and additional attributes describing these nodes or edges. Oftentimes, a common challenge in the visualization of networks is the tendency of larger networks to result in unreadable ‘hair balls’. And even though designers and researchers already developed a variety of visualization techniques to visualize networks (see image), their visualization poses a variety of challenges. How to visualize dynamic networks, i.e. networks that change over time? How to interact with networks to facilitate the exploration of multiple data dimensions? How to visually encode multiple attributes for nodes or edges? How to tell stories with networks? How to convey uncertainty in networks? How can we go beyond the common node-link diagram?

In this advanced data visualization seminar, we want to investigate the above and more questions through work with relational or hierarchical data by developing novel, experimental and aesthetic network visualizations. After a recapitulation of basic information visualization principles and methods (e.g., encoding, interaction), we will focus on the visualization of and interaction with relational/hierarchical data. To do that, the seminar will involve theoretical and practical sessions on data visualization, including the use of D3.js for visualization. Building up on small hands-on assignments, you will develop your own visualization projects in groups or individually. The goal of the course is the development and design of experimental and aesthetic visualization prototypes with a focus on the visualization of relations featuring a self-chosen topic.

Prerequisites:

• theoretical and/or practical experiences with the study and design of data visualization
• experience in coding (especially: HTML, CSS, JavaScript), data analysis (e.g., Python, R, Open Refine), or the use of network visualization (e.g., Gephy) could be very beneficial

Please note: The class will be taught in English. All communication during the seminars as well as literature and auxiliary materials will be in English.