53 research outputs found
Querying and creating visualizations by analogy
Journal ArticleWhile there have been advances in visualization systems, particularly in multi-view visualizations and visual exploration, the process of building visualizations remains a major bottleneck in data exploration. We show that provenance metadata collected during the creation of pipelines can be reused to suggest similar content in related visualizations and guide semi-automated changes. We introduce the idea of query-by-example in the context of an ensemble of visualizations, and the use of analogies as first-class operations in a system to guide scalable interactions. We describe an implementation of these techniques in VisTrails, a publicly-available, open-source system
A Vector Field Design Approach to Animated Transitions
Animated transitions can be effective in explaining and exploring a small number of visualizations where there are drastic changes in the scene over a short interval of time. This is especially true if data elements cannot be visually distinguished by other means. Current research in animated transitions has mainly focused on linear transitions (all elements follow straight line paths) or enhancing coordinated motion through bundling of linear trajectories. In this paper, we introduce animated transition design, a technique to build smooth, non-linear transitions for clustered data with either minimal or no user involvement. The technique is flexible and simple to implement, and has the additional advantage that it explicitly enhances coordinated motion and can avoid crowding, which are both important factors to support object tracking in a scene. We investigate its usability, provide preliminary evidence for the effectiveness of this technique through metric evaluations and user study and discuss limitations and future directions
Reducing Access Disparities in Networks using Edge Augmentation
In social networks, a node's position is a form of \it{social capital}.
Better-positioned members not only benefit from (faster) access to diverse
information, but innately have more potential influence on information spread.
Structural biases often arise from network formation, and can lead to
significant disparities in information access based on position. Further,
processes such as link recommendation can exacerbate this inequality by relying
on network structure to augment connectivity.
We argue that one can understand and quantify this social capital through the
lens of information flow in the network. We consider the setting where all
nodes may be sources of distinct information, and a node's (dis)advantage deems
its ability to access all information available on the network. We introduce
three new measures of advantage (broadcast, influence, and control), which are
quantified in terms of position in the network using \it{access signatures} --
vectors that represent a node's ability to share information. We then consider
the problem of improving equity by making interventions to increase the access
of the least-advantaged nodes. We argue that edge augmentation is most
appropriate for mitigating bias in the network structure, and frame a budgeted
intervention problem for maximizing minimum pairwise access.
Finally, we propose heuristic strategies for selecting edge augmentations and
empirically evaluate their performance on a corpus of real-world social
networks. We demonstrate that a small number of interventions significantly
increase the broadcast measure of access for the least-advantaged nodes (over 5
times more than random), and also improve the minimum influence. Additional
analysis shows that these interventions can also dramatically shrink the gap in
advantage between nodes (over \%82) and reduce disparities between their access
signatures
Information access representations and social capital in networks
Social network position confers power and social capital. In the setting of
online social networks that have massive reach, creating mathematical
representations of social capital is an important step towards understanding
how network position can differentially confer advantage to different groups
and how network position can itself be a source of advantage. In this paper, we
use well established models for information flow on networks as a base to
propose a formal descriptor of the network position of a node as represented by
its information access. Combining these descriptors allows a full
representation of social capital across the network. Using real-world networks,
we demonstrate that this representation allows the identification of
differences between groups based on network specific measures of inequality of
access
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