Kathryn Gray

Felice De Luca, Iqbal Hossain, Kathryn Gray et al.

Human subject studies that map-like visualizations are as good or better than standard node-link representations of graphs, in terms of task performance, memorization and recall of the underlying data, and engagement [SSKB14, SSKB15]. With this in mind, we propose the Zoomable Multi-Level Tree (ZMLT) algorithm for multi-level tree-based, map-like visualization of large graphs. We propose seven desirable properties that such visualization should maintain and an algorithm that accomplishes them. (1) The abstract trees represent the underlying graph appropriately at different level of details; (2) The embedded trees represent the underlying graph appropriately at different levels of details; (3) At every level of detail we show real vertices and real paths from the underlying graph; (4) If any node or edge appears in a given level, then they also appear in all deeper levels; (5) All nodes at the current level and higher levels are labeled and there are no label overlaps; (6) There are no edge crossings on any level; (7) The drawing area is proportional to the total area of the labels. This algorithm is implemented and we have a functional prototype for the interactive interface in a web browser.

Kathryn Gray, Daniel Smolyak, Sarkhan Badirli et al.

Detecting anomalous activity in human mobility data has a number of applications including road hazard sensing, telematic based insurance, and fraud detection in taxi services and ride sharing. In this paper we address two challenges that arise in the study of anomalous human trajectories: 1) a lack of ground truth data on what defines an anomaly and 2) the dependence of existing methods on significant pre-processing and feature engineering. While generative adversarial networks seem like a natural fit for addressing these challenges, we find that existing GAN based anomaly detection algorithms perform poorly due to their inability to handle multimodal patterns. For this purpose we introduce an infinite Gaussian mixture model coupled with (bi-directional) generative adversarial networks, IGMM-GAN, that is able to generate synthetic, yet realistic, human mobility data and simultaneously facilitates multimodal anomaly detection. Through estimation of a generative probability density on the space of human trajectories, we are able to generate realistic synthetic datasets that can be used to benchmark existing anomaly detection methods. The estimated multimodal density also allows for a natural definition of outlier that we use for detecting anomalous trajectories. We illustrate our methodology and its improvement over existing GAN anomaly detection on several human mobility datasets, along with MNIST.