Deriving Structure from Graphs for Visual Data Exploration, Analysis, and Knowledge Discovery

May 6, 2021
1:00-1:40 pm ET
Sococo Halligan 102, Zoom
Speaker: Ashley Suh
Host: Remco Chang


Quals talk:

Graphs are ubiquitous for representing complex relationships between entities across a variety of domains. The problem of effectively visualizing and analyzing graphs has a rich literature, in which some approaches may leverage the derived information or underlying structure of a graph to improve its visual layout, and thus its analytic capabilities. However, as the demand rises for researchers and data scientists to make sense of highly complex and multi-dimensional graph data, so does the need for both sophisticated yet generalizable graph visualization techniques and graph analytics tools.

In this talk, I will discuss how visualization and visual analytics systems can make use of the derived structure from graphs to guide data exploration and analysis. In particular, I will cover two visualization systems I have developed in which the topology of a graph is extracted for two goals: (1) to automatically improve a graph's visual layout and interactive exploration capabilities, and (2) to automatically suggest related or interesting data attributes from complex graph data -- such as knowledge graphs. Finally, I will conclude with a discussion of future research in which both of these systems' goals can be combined to provide a state-of-the-art visual graph data analytics system.

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