Network Visualization and Visual Network Analytics

Networks (or graphs how they are called usually in computer science) are one of the most important and also most challenging data sets in information visualization. We may here distinguish between graphs and multivariate networks. A (simple) graph G = (V,E) consists of a finite set of vertices (or nodes) V and a set of edges E where each one connects two of the vertices/nodes. Whereas, a multivariate network N consists of an underlying graph G plus additional attributes that are attached to the nodes and/or edges. Traditionally, Graph Drawing (GD) algorithms compute a 2D/3D layout of the nodes and the edges, mainly based on so-called node-link diagrams. They play a fundamental role in network visualization. Particular graph layout algorithms can give an insight into the topological structure of a network if properly chosen and implemented. The graph readability is affected by quantitative measurements.
In Information Visualization (InfoVis), the research does not solely focus on the pretty representation of the graph/network. The sheer size and complexity of eventually multivariate networks demand for other solutions to display those networks. The InfoVis community addresses those issues by visualization approaches that provide filtering and advanced interaction possibilities in order to reduce the number of graph elements under consideration as well as by methods to visually analyze attributes in context of the underlying graph topology; often in combination with computational methods from network analytics, such as network centralities.

We focus in our research on multivariate network visualization and on visual analysis of heterogeneous networks as well. We are broadly interested in developing novel visual representations and interaction techniques for these two network types that both are crucial for many application domains, such as biological or social network analysis.