Skeletal Images as Visual Cues in Graph Visualization

Abstract

The problem of graph layout and drawing is fundamental to many aproaches to the visualization of relational information structures. As the data set grows, the visualization problem is compounded by the need to reconcile the user's need for orientation cues with the danger of information overload. Put simply: How can we limit the number of visual elements on the screen so as not to overwhelm the user yet retain enough information that the user is able to navigate and explore the data set confidently? How can we provide orientational cues so that a user can understand the location of the current viewpoint in a large data set? These are problems inherent not only to graph drawing but information visualization in general. We propose a method which extracts the significant features of a directed acyclic graph as the basis for navigation 1 .

Flows of Knowledge in Citation Networks

“…Instead, we mostly take our inspiration from a different notion of flows, in river streams, as defined by Strahler [26]. Limited to binary trees, this notion has seen a few extensions [3,12,18] with applications to graph visualization. These versions use flows to highlight and extract most relevant paths in DAGs and trees and relatively place elements one to another.…”

“…We defined above our citations graphs to be DAGs, and fortunately, Strahler numbers have also been extended to DAGs [12,18]. Herman et al [18] proposes a generic framework to compute the importance K of nodes in DAGs -including Strahler numbers -such as:…”

“…Herman et al [18] proposes a generic framework to compute the importance K of nodes in DAGs -including Strahler numbers -such as:…”

“…Instead, Herman et al [18] propose in their framework a Flow metric for DAGs to emphasize the distribution of information to their successor such as:…”

Étude d'algorithmes en visualisation et exploration de grands graphes à l'aide de valuations /

“…Une approche utilisée pour la visualisation de graphes est l'association de propriétés graphiques à des valuations [32] (Fig 3.5). Par exemple la taille de la représen- Les couches au haut de la hiérarchie représentent la strucutre générale de l'image tandis que les couches plus basses représentent les détails.…”