HyperStorylines: Interactively untangling dynamic hypergraphs

Abstract

We present the design and evaluation of HyperStorylines, a technique that generalizes Storylines to visualize the evolution of relationships involving multiple types of entities such as, for example, people, locations, and companies. Datasets which describe such multi-entity relationships are often modeled as hypergraphs, that can be difficult to visualize, especially when these relationships evolve over time. HyperStorylines builds upon Storylines, enabling the aggregation and nesting of these dynamic, multi-entity relationships. We report on the design process of HyperStorylines, which was informed by discussions and workshops with data journalists; and on the results of a comparative study in which participants had to answer questions inspired by the tasks that journalists typically perform with such data. We observe that although HyperStorylines takes some practice to master, it performs better for identifying and characterizing relationships than the selected baseline visualization (PAOHVis) and was preferred overall.

DGSVis: Visual Analysis of Hierarchical Snapshots in Dynamic Graph

“…With the increasing scale of dynamic graph data, traditional sequential node-link and matrix-based diagrams fail to display all the graphs in a plane which is space limited (e.g., computer and mobile screen, etc). To address above problems, researches design many techniques to visualizing dynamic graphs such as graph animation [1,11,32], parallel-based node-link diagram [5,14], graph snapshots [6,43], graph navigation [19,25], graph projection [13], set-based graph [34], graph clustering [17,47], and hypergraph visualization [29,41]. For example, animating the graphs according to the timestamps in dynamic graph evolution analysis and structure analysis can reduce the showing space, transferring the time for space [1].…”

Geo-Storylines: Integrating Maps Into Storyline Visualizations

“…The visualization of dynamic hypergraphs, that represent the evolution of relationships over time, is an active research field [22]. The three more recent visualizations are PAOHViz [55], Hyper-Matrix [21] and HyperStorylines [41]. The first two adopt a matrix approach and represent entities as rows and relationships in columns.…”

“…In this paper we set out to create visualizations that can combine entities' dynamic relationships with the geospatial context they occur in. We chose to do this by integrating geospatial information into Storylines as their layout prioritizes grouping of related entities and allows for a more compact and easy-to-analyze visualization than PAO-HVis [41]. It is this relative simplicity of Storylines that makes them a promising candidate for integrating additional geospatial information.…”

“…For example, they have been used to visualize how developers interact on a code repository [36], to illustrate spatio-temporal patterns of fixations in eye tracking [9], and to visualize the evolution of communities in dynamic social networks [44]. More recently, HyperStorylines has generalized Storylines to represent complex relationships between any type of entity in a hypergraph [41].…”

Visualización de datos

“…Curiosamente, es la tira cómica y no el mapa de Minard el que ha derivado en la aparición de una técnica de visualización de datos denominada storyline (Ogawa y Ma, 2010); los autores adaptaron la representación propuesta por Munroe al análisis de las interacciones entre desarrolladores de un proyecto software. Desde entonces se han sucedido diversas propuestas para la automatización de la creación de este tipo de diagramas (T. Chen et al, 2012;Tanahashi y Ma, 2012;van Dijk et al, 2017;Tang et al, 2018;Padia et al, 2018;Tang et al, 2020;Araya et al, 2021).…”