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Clayton Morrison

dblp:385/1161 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Visualization and visual analytics · 100%
Theoretical computer science
1 paper
Graph algorithms and graph theory · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Visualization and visual analytics
graph visualization
0.812024
An Overview+Detail Layout for Visualizing Compound Graphs · IEEE VIS 2024
Visualization and visual analytics › focus+context visualization
overview+detail interface
0.812024
An Overview+Detail Layout for Visualizing Compound Graphs · IEEE VIS 2024

Methods — techniques the papers use, named apart from their topics

overview+detail · 1.5modified tree layout · 1.5
YearPublicationVenuePosition
2024 An Overview+Detail Layout for Visualizing Compound Graphs
abstract
Compound graphs are networks in which vertices can be grouped into larger subsets, with these subsets capable of further grouping, resulting in a nesting that can be many levels deep. In several applications, including biological workflows, chemical equations, and computational data flow analysis, these graphs often exhibit a tree-like nesting structure, where sibling clusters are disjoint. Common compound graph layouts prioritize the lowest level of the grouping, down to the individual ungrouped vertices, which can make the higher level grouped structures more difficult to discern, especially in deeply nested networks. Leveraging the additional structure of the tree-like nesting, we contribute an overview+detail layout for this class of compound graphs that preserves the saliency of the higher level network structure when groups are expanded to show internal nested structure. Our layout draws inner structures adjacent to their parents, using a modified tree layout to place substructures. We describe our algorithm and then present case studies demonstrating the layout's utility to a domain expert working on data flow analysis. Finally, we discuss network parameters and analysis situations in which our layout is well suited.
Chang Han, Justin Lieffers, Clayton Morrison, Katherine E. Isaacs
IEEE VIS3