# HOTVis: Higher-Order Time-Aware Visualisation of Dynamic Graphs

## TL;DR

HOTVis introduces a novel static visualization method for dynamic graphs that incorporates higher-order causal paths, enabling clearer insights into temporal and causal structures within complex networks.

## Contribution

It presents a new visualization algorithm that uses higher-order models to embed causal path information into static graph layouts.

## Key findings

- Enhances interpretability of dynamic graphs by highlighting causal patterns.
- Combines static visualization simplicity with time-aware causal information.
- Facilitates analysis of temporal influence and clustering in networks.

## Abstract

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.