The 2010's

Persistent Homology Guided Force-Directed Graph Layouts

In this paper, we leverage persistent homology features of an undirected graph as derived information for interactive manipulation of force-directed layouts. We first discuss how to efficiently extract 0-dimensional persistent homology features from both weighted and unweighted undirected graphs. We then introduce the interactive persistence barcode used to manipulate the force-directed graph layout by adding and removing contracting and repulsing forces generated by the persistent homology features, eventually selecting the set of persistent homology features that most improve the layout. Continue reading

Visual Inspection Of Dbs Efficacy

A key problem in Deep Brain Stimulation (DBS) is determining the optimal parameters for clinical outcome. Current knowledge does not provide a complete deterministic model of DBS neurophysiology. While multiple parameters may influence clinical outcomes in DBS, this paper explores spatial correlation of volume of tissue activated (VTA) to Unified Parkinson's Disease Rating Scale (UPDRS) scores. Continue reading

Propagate And Pair: A Single-Pass Approach To Critical Point Pairing In Reeb Graphs

Pairing critical points enables forming topological fingerprints, known as persistence diagrams, that provides insights into the structure and noise in data. In this paper, we discuss two algorithmic approaches for pairing critical points in Reeb graphs, first a multipass approach, followed by a new single-pass algorithm, called Propagate and Pair. Continue reading

CAREER: Discovering Structure in Uncertainty: Using Topology for Interactive Visualization of Uncertainty

This project addresses two important scientific questions: how to effectively use topology to extract features from ensembles; and how to design visualizations for domain experts that efficiently communicate the features. To extract features from an ensemble, the project will investigate new methods of robustly comparing and contrasting the topology of multiple ensemble realizations. Then, it will design new visualization methods for efficiently and effectively comparing and exploring the features and variations within ensembles.

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Visual Detection Of Structural Changes In Time-Varying Graphs Using Persistent Homology

In this paper, we propose a novel method using persistent homology to quantify structural changes in time-varying graphs. Specifically, we transform each instance of the time-varying graph into a metric space, extract topological features using persistent homology, and compare those features over time. We provide a visualization that assists in time-varying graph exploration and helps to identify patterns of behavior within the data. Continue reading