Visual Analytics and Imaging Laboratory (VAI Lab)
Computer Science Department, Stony Brook University, NY

Analytics of Heterogeneous Data using Hypergraph Learning

Abstract: For real-world learning tasks (e.g., classification), graph-based models are commonly used to fuse the information distributed in diverse data sources, which can be heterogeneous, redundant, and incomplete. These models represent the relations in different datasets as pairwise links. However, these links cannot deal with high-order relations which connect multiple objects (e.g., in public health datasets, more than two patient groups admitted by the same hospital in 2014). In this paper, we propose a visual analytics approach for the classification on heterogeneous datasets using the hypergraph model. The hypergraph is an extension to traditional graphs in which a hyperedge connects multiple vertices instead of just two. We model various high-order relations in heterogeneous datasets as hyperedges and fuse different datasets with a unified hypergraph structure. We use the hypergraph learning algorithm for predicting missing labels in the datasets. To allow users to inject their domain knowledge into the model-learning process, we augment the traditional learning algorithm in a number of ways. Besides, we also propose a set of visualizations which enable the user to construct the hypergraph structure and the parameters of the learning model interactively during the analysis. We demonstrate the capability of our approach via two real-world cases.

Teaser: This is an example of predicting hospital readmission levels with five heterogeneous tables:

The readmission table in (a) is partially labeled as shown in the last column. Traditional graph-based methods use pairwise links to model pairwise relations among the tables whch has limitations when there are multiple relationships. Panel (a) shows our hypergraph model where the hyperedges are able to encode higher-order relationships.

Video: Watch it to get a quick overview:

Paper: C. Xie, W, Zhong. W. Xu, K. Mueller, "Analytics of Heterogeneous Data using Hypergraph Learning," ACM Trans.on Intelligent Systems and Technology, 10(1), 1-26, 2019. pdf

Funding: NSF grant IIS-1527200