NII Shonan Meeting


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Dynamic networks, and dynamic information in general, are an important topic across many domains. Often, the data can be expressed as a network that evolves over time.

In order to support proper visual analysis, we must have effective visualizations that draw the dynamic perspective of these networks in a meaningful and comprehensible way. For the visualization to be successful, the user of the system must be able to follow the evolving data.

In psychology and geography, these concepts have been explored in the context of humans navigating physical environments with maps with the internal representation of the space inside the mind of the human known as the mental map or cognitive maps.  Thus, we can separate out the mental map and drawing stability with the mental map being the internal representation and drawing stability the external representation present in the visualization.

Early work in the mental map of information spaces concentrated on users following changes in a network: either through dynamic data or interaction with this data. One dimension of comprehensibility of dynamic information is information stability. In the early 1990’s, Misue et al. proposed methods for enhancing the stability of dynamically evolving graphs.  In particular, the work examined what should happen to unaffected areas of the network once a local change had been made to the network.

One of the most common interpretations of preserving the mental map is the notion that nodes and edges of the graph should move as little as possible between successive time periods in the plane. Archambault and Purchase revealed quantitative benefits of this definition as it helps users revisit specific nodes in a dynamic graph and follow specific paths in a graph as the data evolves over time. Preserving the mental map helps users offload information to the representation as they understand that it will remain in the same place, unless the network changes substantially. In dynamic graph drawing, the majority of methods for drawing dynamic graphs in a stable way have taken the simple definition of keeping nodes in relatively the same area of the plane. However, in the original definitions of mental map preservation, more complex measures were considered, including topological properties of the drawing.

Although the mental map in information visualization has frequently been associated with dynamic data, supporting the mental map is also important for interaction. The mental map can be affected not only by changes in the representation, but by the combination of representation, interaction operations performed by the user, and the associated cognitive processes. As such, it is not only important to engage information visualization researchers with this concept, but HCI experts and researchers in immersive analytics need to consider visualizations that support the cognitive map from an interaction perspective.

Thus, while there is already a significant body of research and a range of models for mental map and data stability, several challenges arose in recent years prompt us to revisit the concepts and to develop new approaches. These challenges concern scalability, the applicability of the models in application areas, as well as the technology of the environment in which the network analysis is performed:

  • The size and complexity of the data that is represented has increased dramatically over the last years. While layout algorithms scale well and can draw hundreds of thousands of nodes and edges, how we support the cognitive models of networks needs to be adjusted to the change in scale. On the other hand, when methods like aggregation or clustering are used to reduce visual complexity, the resulting visualization will need different, more complex concepts for mental map preservation with respect to the relation between representation and raw data.
  • Applications might require specific adaptions or requirements to mental map preservation. Additional data annotations and semantics play a crucial role for expert users and might need to be taken into account. Many of the original models were written from an algorithmic perspective, closely related to the corresponding network drawing approaches. While this allows for easy integration into dynamic network visualization implementations, it may not fit with specific application requirements.
  • New technologies such as wall displays, table top displays, and 3D environments are available that facilitate novel visualization and interaction methods, but might be a game changer in terms of mental map preservation as existing concepts may not be directly transferable to these new devices.

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