Year: 2018

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

Inferring Quality In Point Cloud-Based 3D Printed Objects Using Topological Data Analysis

Assessing the quality of 3D printed models before they are printed remains a challenging problem, particularly when considering point cloud based models. This paper introduces an approach to quality assessment, which uses techniques from the field of Topological Data Analysis to compute a topological abstraction of the eventual printed model. This abstraction enables investigating certain qualities of the model, with respect to print quality, and identify potential anomalies that may appear in the final product. Continue reading