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 a social network setting, understanding data from Twitter and Facebook can clarify the interaction between people and the evolution of events in real time. In a biological setting, genes and proteins interact and these interactions can change depending on an experimental treatment and expression levels of the genes change with time. An analysis of these changes can then for example be used to detect disease conditions, understand their mechanism, and treat them.  In a computer network scenario, links can go down and new connections are made. In finance, trades can be expressed as a network and can be interpreted along with information about the evolving market around them. Regulators and market participants can then monitor and analyze market behavior, e.g. to detect fraud and suspicious behavior. In all of these applications, we must have effective visualizations that draw the dynamic perspective of these networks in a meaningful and comprehensible way. In order 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. In this work, a cognitive map is the internal representation of the physical space inside the mind of the human. In a dynamic information context, the cognitive map is the internal representation of the information space that is evolving over time. Thus, we can begin to 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. The motivation for these approaches was to increase the comprehensibility of dynamic data: if network structure changes in a local area of the plane, then areas that do not change should remain stable. Many interpretations of this concept of preserving the mental map have been considered and expanded on through the years.

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. Moving a cluster of nodes from one corner of the screen to the other will affect drawing stability in the classical definition, but might preserve the quality of the mental map perfectly. When interacting with data, changes to the representation should only influence the area interacted with and not the entire data set as a whole. When information goes off screen because of an interaction, one would expect it to come back on screen if the interaction is reverted. 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.

In this seminar, our goal is to revisit some of the mental map preservation definitions and to develop new definitions for drawing stability that support the comprehensibility of dynamic data. We plan to go beyond the basic definition of preserving node location in space to other definitions that can support the cognitive map of the user as they navigate the dynamically evolving information space. More specifically, we intend to pursue the following research questions:

1. What are new metrics and models that cover stability and mental map quality in the light of above mentioned challenges?
2. What are new algorithms that can be developed to support the cognitive maps of users visualising dynamic data?
3. What new methods need to be developed that better support the cognitive maps of users exploring information in specific application domains?
4. Given new display and interaction technologies, are there new approaches for preserving the mental map that better supports the exploration of networks when using these devices?

The development of techniques that support a user’s mental map require the combination of expert knowledge on network / information visualisation principles and algorithms, expertise in perception and cognitive processes, as well as a good definition of requirements from practical applications. The workshop aims  at examining information visualisation techniques that are better able to support the mental map of the user. Our workshop intends to study supporting the cognitive map both in dynamic network settings and with respect to interaction with devices.  More specifically, the aims of the seminar are as follows:

1. To bring experts in the fields information visualisation, graph drawing, interaction and devices, psychol- ogy, and relevant application We intend to have an international audience from Europe, Asia, the Americas, and Australia that have information visualisation problems where cognitive maps of the information space should be supported.
2. To rethink algorithms that compute stable representations of dynamic data and to go beyond “keeping unchanging components in relatively the same area of the plane.” What are alternatives for supporting the mental map of the user when investigating dynamic data?
3. To rethink implications of interaction on the mental map of information spaces. How does interacting with information spaces on large screens, table tops, immersive analytics environments impact cognitive maps of information? Are there any special requirements that need to be supported?
4. Psychology and geography have considered the cognitive map. How can their definitions of cognitive maps help us understand the mental map in information visualisation?
5. What are good ways to apply our results to relevant application areas (biology, social networks, and others)?
6. To formulate future research challenges in better supporting the mental map for information visualisation research.