HOTVis: Higher-Order Time-Aware Visualisation of Dynamic Graphs
This addresses a gap in network visualisation for researchers analyzing complex systems with time series data, though it appears incremental as it builds on existing static graph methods.
The authors tackled the lack of graph drawing techniques that incorporate causal topology from temporal ordering in dynamic graphs, presenting a novel algorithm that uses higher-order graphical models to compute time-aware static visualisations, highlighting patterns in causal dynamics.
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.