@conference{715,
  keywords = {Information diffusion, dynamic networks, network visualization, Visualization, Visual analytics},
  author = {Tom Baumgartl and M. Sondag and Velitchko Filipov and Michaela Tuscher and Sandhya Rajendran and Silvia Miksch and D. Archambault and Alessio Arleo and T. von Landesberger},
  title = {Survey on Visualization of Information Diffusion over Networks},
  abstract = {<p>Information Diffusion (ID) describes how a value (e.g., a pathogen, a rumor, a packet) spreads through an underlying “medium” network of elements (e.g., a social or computer network). Understanding the information diffusion process is essential to predicting trends, controlling misinformation, and enhancing decision-making as well as communication strategies. Visual Analytics has shown significant potential in supporting comprehension of ID processes in several domains. These approaches vary greatly in their design, both in terms of data and visual encodings. The variety of designs and application domains motivates this survey, which complements existing surveys with a focus on visualization of transmission processes in contrast to other surveys about visualizing networks. This survey defines types of transmission and medium networks, identifies common visualization principles, categorizes them, and identifies gaps–opportunities for future research.</p>},
  year = {2026},
  journal = {28th Eurographics Conference on Visualization (EuroVis 2026)},
  volume = {45/3},
  pages = {42},
  month = {06/2026},
  address = {Nottingham},
  doi = {10.1111/cgf.70498},
}