A visualisation technique for large temporal social network datasets in Hyperbolic space

• We propose a hyperbolic temporal layout method.
• Our method represents the evolution of relations amongst network actors.
• Our method represents the evolution of structural pattern of a social network.
• We encode network metrics into a temporal layout improving the information density.
• Our method reduces overall visual clutter without topology modification.

Visualisations of temporal social network datasets have the potential to be complex and require a lot of cognitive input. In this paper, we present a novel visualisation approach that depicts both relational and statistical information of evolving social structures. The underlying framework is implemented by the usage of Hyperbolic Geometry to support focus context rendering. The proposed method guarantees representing prominent social actors through scaling their representations, preserves user's mental map, and provides the user to reduce visual clutter by means of filtering.