Small MultiPiles: Piling Time to Explore Temporal Patterns in Dynamic Networks

Abstract

International audienceWe introduce MultiPiles, a visualization to explore time-series of dense, weighted networks. MultiPiles is based on the physical analogy of piling adjacency matrices, each one representing a single temporal snapshot. Common interfaces for visualizing dynamic networks use techniques such as: flipping/animation; small multiples; or summary views in isolation. Our proposed 'piling' metaphor presents a hybrid of these techniques, leveraging each one's advantages, as well as offering the ability to scale to networks with hundreds of temporal snapshots. While the MultiPiles technique is applicable to many domains, our prototype was initially designed to help neuroscien-tists investigate changes in brain connectivity networks over several hundred snapshots. The piling metaphor and associated interaction and visual encodings allowed neuroscientists to explore their data, prior to a statistical analysis. They detected high-level temporal patterns in individual networks and this helped them to formulate and reject several hypotheses

HiPiler: Visual Exploration of Large Genome Interaction Matrices with Interactive Small Multiples

“…Our work is most inspired by MultiPiles [3]—an interface that employs the visual and interactive metaphor of piling adjacency matrices and exploring these piles to visualize time sequences in dynamic networks. We integrate the piling metaphor and some of the exploration features from MultiPiles but heavily extend upon them by introducing linear ordering, multi-dimensional arrangements, clustering, filtering, and grouping approaches for exploring many snippets.…”

“…To support the exploration of large numbers of snippets, HiPiler applies and extends upon the piling metaphor of Multipiles [3]. Snippets are stacked into a pile featuring a cover matrix that shows a summary of the stacked snippets.…”

DyNetVis

“…The limitations of this method includes a requirement for more screen space and a difficulty to compare snapshots. Other methods to enhance visual pattern detection in dynamic networks include employing 'piling' metaphors [3], adjacency lists, or representing network snapshots as high-dimensional points that are projected in two dimensions [20]. There are also strategies as Temporal Trends [4] and Heatmap Grids [9] that are used to perform visual analytics of firewall log events and dense networks in astronomy and neurology, respectively.…”

Interactive web application for exploring matrices of neural connectivity

“…Other recent advances in visualization of neural data include the folding of time curves to visualize patterns of temporal activation [4] and stacking of adjacency matrices representing temporal snapshots to visualize changes in networks over time [5]. While these tools offer great flexibility and exploratory depth in dealing with time series data, we desired a simpler, more lightweight approach to visualize connectivity matrices which were constant across a time period.…”

“…While these tools offer great flexibility and exploratory depth in dealing with time series data, we desired a simpler, more lightweight approach to visualize connectivity matrices which were constant across a time period. We wished to implement strategies similar to the interactive pruning [5] and rapid exploratory nature of the above tools. We also desired a tool agnostic to the underlying data matrix, enabling the use of whichever connectivity value the researcher desires.…”