The workshop will focus on the following subtopics specifically (however other topics may be included):
Multilayer Networks have been known in visualization under various names and concepts. Therefore, there is a need for a unified visualization framework for Multilayer Network visualization, interaction and analytics, that focuses on the visualization challenges raised by the underlying mathematical framework.
Multilayer Networks raise the need to develop new visualization techniques that allow for the display of the complexity of the networks and their structure.
The diverse range of application domains results in a vast range of complex data sets and novel problems that may be best addressed by new visualization approaches. The existing body of Multilayer Network research in the fields of Complex Systems and Social Network Analysis also provides additional tools and analytical approaches to these problems.
The complex systems which are modelled as Multilayer Networks often are multivariate in nature. Encoding and visualization of these attributes within the multilayer structure, possibly as nodes of another layer, raises many possibilities.
Additionally understanding the analytical relationship between layers (with respect to structure and/or attributes) and supporting layer comparison raises many interesting challenges and opportunities for visualization. Finally, network science provides many new metrics for the multilayer use case, which offer new possibilities for visual analytics.
Dealing with layers as objects in a visual analytics system results in new tasks, and possibly interaction techniques.
The complexity of the network structure and its visual encoding raises the challenges of assessing the perceptual and cognitive aspects when interactively visualizing the networks. Many of the systems, which are developed within the context of application domains, lack a thorough empirical evaluation. The existing methodologies and rigour found in the information visualization and HCI communities, need to be adapted for the Multilayer Networks use case.