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Title: Visual 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. Thesemodels 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 article, we propose a visual analytics approach forthe 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. In addition, 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. Here, we demonstrate the capability of our approach via two real-world cases.

Authors:
 [1];  [1];  [2];  [1]
  1. Stony Brook Univ., Stony Brook, NY (United States)
  2. Brookhaven National Lab. (BNL), Upton, NY (United States)
Publication Date:
Research Org.:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (SC-21)
OSTI Identifier:
1496572
Report Number(s):
BNL-211294-2019-JAAM
Journal ID: ISSN 2157-6904
Grant/Contract Number:  
SC0012704
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
ACM Transactions on Intelligent Systems and Technology
Additional Journal Information:
Journal Volume: 10; Journal Issue: 1; Journal ID: ISSN 2157-6904
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; hypergraph learning; data fusion; high-dimensional data

Citation Formats

Xie, Cong, Zhong, Wen, Xu, Wei, and Mueller, Klaus. Visual Analytics of Heterogeneous Data Using Hypergraph Learning. United States: N. p., 2018. Web. doi:10.1145/3200765.
Xie, Cong, Zhong, Wen, Xu, Wei, & Mueller, Klaus. Visual Analytics of Heterogeneous Data Using Hypergraph Learning. United States. doi:10.1145/3200765.
Xie, Cong, Zhong, Wen, Xu, Wei, and Mueller, Klaus. Sat . "Visual Analytics of Heterogeneous Data Using Hypergraph Learning". United States. doi:10.1145/3200765.
@article{osti_1496572,
title = {Visual Analytics of Heterogeneous Data Using Hypergraph Learning},
author = {Xie, Cong and Zhong, Wen and Xu, Wei and Mueller, Klaus},
abstractNote = {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. Thesemodels 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 article, we propose a visual analytics approach forthe 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. In addition, 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. Here, we demonstrate the capability of our approach via two real-world cases.},
doi = {10.1145/3200765},
journal = {ACM Transactions on Intelligent Systems and Technology},
issn = {2157-6904},
number = 1,
volume = 10,
place = {United States},
year = {2018},
month = {12}
}

Journal Article:
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