Michaela Tuscher

The Avant‐Garde of the early 20th‐century was characterized by thriving artistic collaborations that gave rise to overlapping movements such as Expressionism, Cubism and Futurism. Although exhibitions acted as central hubs, the resulting artists' networks remain difficult to investigate due to inconsistent documentation, uneven temporal coverage, and rapidly shifting geographic contexts. We introduce Kandinsky , a Visual Analytics approach tailored to uncovering Avant‐Garde exhibition networks. Kandinsky combines a radial clustering layout that captures cohesive artistic ensembles with coordinated views, situating exhibition activity across time and region to enable a structured analysis of historically situated artistic relations. A task‐based validation and usability study with ten experts, supported by an extended case study, show that Kandinsky traces structural communities, transnational and temporal relations, and previously unrecognized patterns. This offers a data‐driven complement to traditional art‐historical inquiry.

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.

Art history traditionally relies on qualitative methods. However, the increasing availability of digitized archives has opened new possibilities for research by integrating visual analytics. This survey presents a comprehensive review of the intersection between art history and visual analytics, focusing on network visualization and how it supports researchers in analyzing and understanding complex art historical relationships through nodes (e.g., artists, artworks, institutions) and edges (the relationships between them). We explore how these approaches enable dynamic analysis, offering novel perspectives on artistic influence, stylistic evolution, and social interactions within the art world. Through this, we also examine wedges, a metaphor for the friction often present in art history between individuals and institutions. These tensions, which have historically played a pivotal role in shaping artistic movements, are now better understood through the lens of network visualization, revealing how conflicts and power dynamics influenced the development of art. Through a hierarchical categorization of the literature, we outline saturated problems and research areas as well as ongoing challenges in art historical research. Furthermore, we highlight the potential of visual analytics to bridge the gap between traditional qualitative research and modern computational analysis, offering interactive exploration, temporal analysis, and complex network visualization. We provide a structured foundation for future research in art history, emphasizing the value of network visualization in enriching the understanding of art history.