  Network visualisation techniques are important tools for the exploratory
analysis of complex systems. While these methods are regularly applied to
visualise data on complex networks, we increasingly have access to time series
data that can be modelled as temporal networks or dynamic graphs. In dynamic
graphs, the temporal ordering of time-stamped edges determines the causal
topology of a system, i.e., which nodes can, directly and indirectly, influence
each other via a so-called causal path. This causal topology is crucial to
understand dynamical processes, assess the role of nodes, or detect clusters.
However, we lack graph drawing techniques that incorporate this information
into static visualisations. Addressing this gap, we present a novel dynamic
graph visualisation algorithm that utilises higher-order graphical models of
causal paths in time series data to compute time-aware static graph
visualisations. These visualisations combine the simplicity and
interpretability of static graphs with a time-aware layout algorithm that
highlights patterns in the causal topology that result from the temporal
dynamics of edges.
